A multi-objective particle swarm optimization algorithm based on three status coordinating searching(TC-MOPSO) is presented. The three status strategy proposed for choosing guides is useful to coordinate local and global search capability. The traditional external archives update mechanism is improved and three kinds of mutation factors are introduced, which contribute to making the Pareto solutions have a better distribution. By solving several standard test functions and comparing with three classical multi-objective optimization algorithms, it is proved that the new algorithm has high competition in terms of convergence and diversity metrics. Finally, the influence of the regionalism coefficient on the performance of the proposed algorithm is analyzed.