Considering the problem of the inadequate search ability for constraint boundary particles in the boundary region, a constrained multi-objective particle swarm optimization algorithm based on self-adaptive evolutionary learning is presented. The evolutionary learning formulas of multi-objective particle swarm optimization algorithm are modified according to the constraint violation level of infeasible particles, so that the algorithm’s search ability is enhanced greatly in the constraint boundary region．Furthermore, a dynamic distribution maintenance strategy for Pareto front based on the crowding distance is adopted to improve the distribution of Pareto front without any increase in the algorithm’s complexity．The experimental results show that the Pareto front obtained by the proposed algorithm has better convergence, distribution and diversity.