Multi-objective particle swarm optimization(MOPSO) algorithm is laborious to choose well-distributed optimal solutions while maintaining convergence. In this paper, an improved MOPSO based on dual distance is proposed to alleviate the above issues. It defines the neighborhood of a particle according to the average distance of the population, and thus the number of neighbors is the level of the particle. The more there are neighbors, the higher the level is. The average distance of two particles is equal so that with combining the crowding distance of swarm, the optimal particle is chosen and the extend archive is updated. In addition, this algorithm combines the mutation behavior of particles to avoid local space. In the experiment, the proposed algorithm is compared with several state-of-the-art algorithms, demonstrating that this algorithm achieves good performance in distribution and diversity of swarm. Finally, the algorithm is applied to the environmental/economic scheduling model in power systems, which can obtain well-distributed optimal solutions.