A self-adaptive multi-objective particle swarm optimization algorithm based on the swarm distribution characteristic is proposed. Firstly, the interquartile range(IQR) is applied to analyze the distribution features of archive in the decision space and classify the state of evolution, for guiding global leader selecting. Then, a swarm variation operator is proposed to rearrange particles dynamically. Finally, according to the evolution state, an archive maintaining strategy is designed to achieve a balance between distribution and convergence. The proposed algorithm is compared with 7 state-of-the-art multi-objective optimization algorithms on ZDT, DTLZ and CEC09 Benchmarks. The indicators of IGD, Spread and ER show that the proposed algorithm has certain advantages over other algorithms in terms of convergence and distribution.