In order to improve the convergence and diversity of the Pareto optimal set in multi-objective optimization algorithms,a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution(dMOPSO-DE) is proposed, in which the direction angle is presented to generate a set of direction vectors for maintaining the uniform distribution of the swarm.To avoid getting trapped into a local Pareto optimal front, decomposition-based strategy and differential evolution operator are used to generate the global best leader.Moreover, particle memory re-initialization is applied to enhance the diversity of the swarm.The preliminary results show that, compared with Non-dominated sorting genetic algorithm-II(NSGA-II),multi-objective particle swarm optimizer (MOPSO), multi-objective particle swarm optimizer based on decomposition(dMOPSO) and multi-objective evolutionary algorithm based on decomposition and differential evolution(MOEA/D-DE), the proposed algorithm has good performance on convergence and diversity.