Abstract: In this paper, a multi-objective decomposition particle swarm optimization algorithm (D-CLMOPSO) based on comprehensive learning strategy is proposed, which uses a comprehensive learning strategy to solve multi-objective problems, so as to avoid premature convergence. The dominant particles are updated by decomposition to enhance the solution Distribution; Archiving mechanism to store the non-dominated solution in the optimization process, and using polynomial variation to avoid falling into the local optimum. Finally, the proposed method is compared with the three multi-objective evolutionary algorithms. The results show that the proposed algorithm has good performance on most of the test problems.