An improved multi-objective particle swarm optimization algorithm based on multiple strategies(MIMOPSO) is proposed for the optimization mechanism of particle swarm optimization, which can further improve the convergence and distribution.First, a heterogeneous learning mode and a leadership of the elite particles to individual learning pattern are established to prompt the population to find true Pareto optimal solutions quickly.Then, the variation and disturbance to the population strategy of the external file redundancy mechanism is used to enhance the diversity of the solution and avoid the premature phenomenon of the algorithm.Experimental results show that, comparing with other several kinds of optimization algorithm, the proposed algorithm has better convergence and diversity.