In order to keep the balance between the global and local searching abilities, an adaptive multi-objective particle swarm optimization algorithm based on Gaussian chaotic mutation and elite learning is proposed. Firstly, a method to detect the convergence state is proposed, then the inertia weight and acceleration coefficients are adjusted adaptively. Then, when the swarm is judged to be in the stagnation state, a mutation operator with the features of Gaussian function and chaotic sequence is proposed to help the swarm jump out from local optimum and enhance the global search. Moreover, the elite solutions in the external archive learn with each other to enhance the local search ability. The simulations on several standard test multi-objective problems show the effectiveness of the proposed method.