Compared with ensemble learning, the ensemble pruning is used to search for the optimal subset among multiple classifiers to improve the generalization performance of the classifier and simplify the ensemble process. In order to improve generalization performance and simplify ensemble process, ensemble pruning is used to search an optimal subset in multiple classifiers. It has attracted widespread concern, and it is significant to reduce the complex of ensemble learning. In recent years, researchers have proposed Pareto ensemble pruning (PEP) which considers both the classification performance and the number of base learners, and solves the two goals as the bi-objective optimization. However, Pareto ensemble pruning method ignores the diversity among classifiers, which would cause relatively large similarity among classifiers. In the paper, we proposed Pareto ensemble pruning with diversity (PEPD), in which diversity among classifiers is introduced into Pareto ensemble pruning method. The first goal of the proposed method is to maximize classifiers' diversity and their classification performance. The second goal is to minimize the number of base learners. The experimental results show that the PEPD method can obtain higher performance in most cases. And the enhancement is due to diversity's combination when PEPD and PEP have the similarity number of base learners. Experiments show that the PEPD method can obtain higher performance in most cases due to diversity's combination, when PEPD and PEP have the similar number of base learners.