In order to improve the convergence and diversity, a multi-objective particle swarm optimization algorithm based on the interaction of multi-level information is proposed. In this algorithm, the optimization is divided into the standard particle optimization layer, the particle evolution and learning layer and the archive information exchange layer. The particle evolution and learning layer ensures that a better particle position can be acquired in each iteration, while the layer of archive information exchange can provide a better global optimization. With the information interaction between different layers in this algorithm, the convergence and diversity are improved. Comparing this algorithm to the NSGA-II algorithm and the MOPSO algorithm, the results show that the proposed algorithm has better performance and can effectively solve the multi-objective optimization problem.