To address the issue that traditional path planning algorithms cannot effectively solve the multi-objective problem of Automated Guided Vehicle (AGV) in path planning, the standard whale optimization algorithm is improved. Tent chaotic mapping and adaptive nonlinear dynamic inertia weight are introduced into the standard whale optimization algorithm, and the convergence factor and search coefficient are improved. Then, the improved algorithm is combined with the A * algorithm for multi-objective point path planning. The iterative curve and running time of the improved whale optimization algorithm are tested using standard test functions, and a simulation comparison is conducted between the improved whale optimization algorithm and the standard whale optimization algorithm in the same map environment. The results show that, with a fixed population size, the improved whale optimization algorithm has a faster convergence speed and search accuracy compared to the standard whale optimization algorithm.