A multi-objective adaptive chaotic particle swarm optimization(MACPSO) algorithmis proposed. Firstly, on the basis of the chaotic sequence, a new dynamic weighting method is proposed to select the global optimum particle. Then, the calculation method of crowding distance in NSGA-II is improved and applied to a rigorous external archive updating strategy. Finally, an adaptive mutation strategy based on the generational distance is presented for the external archive. The operations above mentioned not only enhance the convergence performance of the proposed algorithm, but also improve the uniformity of the Pareto optimal solution. The experimental results show the effectiveness of the proposed method.