An improved multi-objective particle swarm optimization algorithm is proposed to address the problem that traditional particle swarm algorithms are prone to fall into local optimums in complex environment for the path planning of autonomous underwater vehicles (AUVs). Firstly, the Sine double-sequence perturbation interpolation method is proposed to generate the initial particle swarm to reduce the randomness of initialization. Secondly, the algorithm introduces techniques adaptive differential evolution, iterative local search, and traction operation to enhance the global search capability of the algorithm and avoid falling into local optimality, and smooths the paths by cubic spline interpolation method. Finally, the objective function is designed for the ocean current environment, considering the influence of the route length, smoothness, deflection angle and ocean current, and the penalty function method is applied to solve the constrained optimization problem to find the optimal path. The simulation experimental results show that this algorithm can effectively plan a short-range and high-security path to meet the navigation requirements of AUV in ocean current environment, which verifies the effectiveness and practicality of the proposed algorithm.