In order to improve the convergence and diversity of the multi-objective particle swarm optimization, an improved multi-objective particle swarm optimzation algorithm integrating multiply strategies is proposed. Firstly, decompositions are introduced to increase the diversity of the pareto sets. Then, in the updating of the velocity and location, the“multi-point”variation is introduced. With the increase of the number of iterations, the updating of the location varies according to the corresponding criterion, which avoids the premature phenomenon of the algorithm. Finally, the updated population and the optimal solution set are subjected to non-dominated sorting and stored in the elite external archive. Simulation experiment results show that the proposed algorithm has better overall performance than other four multi-objective optimizers.