In order to solve the problem of poor sensitivity to initial value and weak local search ability of multi-objective particle swarm optimization (MOPSO) algorithm, an improved chaotic multi-objective particle swarm optimization algorithm is proposed. According to the concept of multi-objective optimization problems, the chaos is sensitive to initial value, the characteristics of random initial values determine population, by introducing Henon chaotic map, the improved algorithm is applied to the three typical test functions, and compared with the NSGA Ⅱ and MOPSO algorithm. The simulation results show that the improved multi-objective particle swarm optimization algorithm has better convergence, distribution and uniformity, and is feasible and superior.