In order to keep the balance between the diversity and convergence, a bi-group multi-objective particle swarm optimization algorithm based on diversity metric is propose. Firstly, a diversity metric is introduced to multi-objective particle swarm optimization(MOPSO) algorithm and improved based on its characteristics. Then, the whole swarm is divided to two bi-groups with different searching tasks. One of the groups keeps population's diversity during evolution to search better in the whole search space. The other group keeps its convergence to local search nearby the Pareto front. Further more, the searching behavior of the groups based on the diversity metric is adjusted to balance the diversity and convergence. The simulations on several standard test functions verify the effectiveness of the proposed method.