In order to solve the problems of insufficient convergence and diversity in multiobjective particle swarm optimization, a new multiobjective particle swarm optimization based on double decision and fast stratification(DDFSMOPSO) is proposed. In this algorithm, the external archive is used to store the non inferior solutions generated by iteration, and the double decision strategy combining crowding distance and absolute distance is used to maintain the external archive scale, which makes the excellent particles easy retained and developed in the subsequent evolution process. At the same time, the fast stratification strategy is used to select the global learning samples from the external archiving, which can be used to lead the evolution of particles in the population and promote the particles in the population to move to the real Pareto front. DDFSMOPSO algorithm and three classical multiobjective particle swarm optimization are compared in simulation experiment on some test functions of ZDT and DTLZ series. Experimental results show that compared with other classical algorithms, DDFSMOPSO algorithm has better convergence and diversity. Therefore, DDFSMOPSO can be used as an effective algorithm for solving multiobjective optimization problems.