diff --git a/optuna/multi_objective/samplers/_motpe.py b/optuna/multi_objective/samplers/_motpe.py
index 87b0787e15..9f358b798a 100644
--- a/optuna/multi_objective/samplers/_motpe.py
+++ b/optuna/multi_objective/samplers/_motpe.py
@@ -26,9 +26,33 @@
 class MOTPEMultiObjectiveSampler(BaseMultiObjectiveSampler):
     """Multi-objective sampler using the MOTPE algorithm.
 
-    This sampler is a multiobjective version of :class:`~optuna.samplers.TPESampler`.
+    This sampler is a multi-objective version of :class:`~optuna.samplers.TPESampler`.
 
-    For further information about MOTPE algorithm, please refer to the following paper:
+    .. note::
+        For `v2.4.0 <https://github.com/optuna/optuna/releases/tag/v2.4.0>`_ or later,
+        :class:`~optuna.multi_objective.samplers.MOTPEMultiObjectiveSampler` is deprecated and
+        :class:`~optuna.samplers.TPESampler` should be used instead. The following code shows how
+        you apply :class:`~optuna.samplers.TPESampler` to a multi-objective task:
+
+        .. testcode::
+
+            import optuna
+
+
+            def objective(trial):
+                x = trial.suggest_float("x", -100, 100)
+                y = trial.suggest_categorical("y", [-1, 0, 1])
+                f1 = x**2 + y
+                f2 = -((x - 2) ** 2 + y)
+                return f1, f2
+
+
+            # We minimize the first objective and maximize the second objective.
+            sampler = optuna.samplers.TPESampler()
+            study = optuna.create_study(directions=["minimize", "maximize"], sampler=sampler)
+            study.optimize(objective, n_trials=100)
+
+    For further information about MOTPE algorithm, please refer to the following papers:
 
     - `Multiobjective tree-structured parzen estimator for computationally expensive optimization
       problems <https://dl.acm.org/doi/abs/10.1145/3377930.3389817>`_
diff --git a/optuna/samplers/_tpe/multi_objective_sampler.py b/optuna/samplers/_tpe/multi_objective_sampler.py
index ebd35d1352..6f9ff9f78e 100644
--- a/optuna/samplers/_tpe/multi_objective_sampler.py
+++ b/optuna/samplers/_tpe/multi_objective_sampler.py
@@ -22,9 +22,33 @@ def _default_weights_above(x: int) -> np.ndarray:
 class MOTPESampler(TPESampler):
     """Multi-objective sampler using the MOTPE algorithm.
 
-    This sampler is a multiobjective version of :class:`~optuna.samplers.TPESampler`.
+    This sampler is a multi-objective version of :class:`~optuna.samplers.TPESampler`.
 
-    For further information about MOTPE algorithm, please refer to the following paper:
+    .. note::
+        For `v2.9.0 <https://github.com/optuna/optuna/releases/tag/v2.9.0>`_ or later,
+        :class:`~optuna.samplers.MOTPESampler` is deprecated and
+        :class:`~optuna.samplers.TPESampler` should be used instead. The following code shows how
+        you apply :class:`~optuna.samplers.TPESampler` to a multi-objective task:
+
+        .. testcode::
+
+            import optuna
+
+
+            def objective(trial):
+                x = trial.suggest_float("x", -100, 100)
+                y = trial.suggest_categorical("y", [-1, 0, 1])
+                f1 = x**2 + y
+                f2 = -((x - 2) ** 2 + y)
+                return f1, f2
+
+
+            # We minimize the first objective and maximize the second objective.
+            sampler = optuna.samplers.TPESampler()
+            study = optuna.create_study(directions=["minimize", "maximize"], sampler=sampler)
+            study.optimize(objective, n_trials=100)
+
+    For further information about MOTPE algorithm, please refer to the following papers:
 
     - `Multiobjective tree-structured parzen estimator for computationally expensive optimization
       problems <https://dl.acm.org/doi/abs/10.1145/3377930.3389817>`_
diff --git a/optuna/samplers/_tpe/sampler.py b/optuna/samplers/_tpe/sampler.py
index 4835109ef4..cdb66fdd4c 100644
--- a/optuna/samplers/_tpe/sampler.py
+++ b/optuna/samplers/_tpe/sampler.py
@@ -74,11 +74,15 @@ class TPESampler(BaseSampler):
       <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`_
     - `Making a Science of Model Search: Hyperparameter Optimization in Hundreds of
       Dimensions for Vision Architectures <http://proceedings.mlr.press/v28/bergstra13.pdf>`_
-    - `Multiobjective tree-structured parzen estimator for computationally expensive optimization
-      problems <https://dl.acm.org/doi/10.1145/3377930.3389817>`_
+
+    For multi-objective TPE (MOTPE), please refer to the following papers:
+
+    - `Multiobjective Tree-Structured Parzen Estimator for Computationally Expensive Optimization
+      Problems <https://dl.acm.org/doi/10.1145/3377930.3389817>`_
     - `Multiobjective Tree-Structured Parzen Estimator <https://doi.org/10.1613/jair.1.13188>`_
 
     Example:
+        An example of a single-objective optimization is as follows:
 
         .. testcode::
 
@@ -94,6 +98,28 @@ def objective(trial):
             study = optuna.create_study(sampler=TPESampler())
             study.optimize(objective, n_trials=10)
 
+    .. note::
+        :class:`~optuna.samplers.TPESampler` can handle a multi-objective task as well and
+        the following shows an example:
+
+        .. testcode::
+
+            import optuna
+
+
+            def objective(trial):
+                x = trial.suggest_float("x", -100, 100)
+                y = trial.suggest_categorical("y", [-1, 0, 1])
+                f1 = x**2 + y
+                f2 = -((x - 2) ** 2 + y)
+                return f1, f2
+
+
+            # We minimize the first objective and maximize the second objective.
+            sampler = optuna.samplers.TPESampler()
+            study = optuna.create_study(directions=["minimize", "maximize"], sampler=sampler)
+            study.optimize(objective, n_trials=100)
+
     Args:
         consider_prior:
             Enhance the stability of Parzen estimator by imposing a Gaussian prior when