To improve the diversity and convergence of Pareto front generated by multi objective particle swarm optimization, a detection mechanism for evolutionary state of multi objective particle swarm optimization is presented in this paper. The evolutionary state of the algorithm is assumed by detecting the updating situation of the external Pareto set to get the feedback information to adjust the evolutionary strategy of the algorithm dynamically. It enables the algorithm to take the diversity and convergence of the approximate Pareto front into account in the process of the evolution. Finally, the proposed algorithm shows a good performance compared with other four kinds of equivalence algorithms in the ZDT series test function.