In order to improve the assembly efficiency of pulsating production lines, the whale optimization algorithm is improved to settle the assembly sequence planning problem under pulsating production line mode. Construct a fitness function that includes four evaluation indicators: geometric feasibility, assembly stability, assembly aggregation, and assembly site feasibility, and solve it with the goal of having the lowest fitness. Add Tent chaotic mapping, Cauchy mutation, and adaptive weight parameters to the whale optimization algorithm to enhance global search ability and prevent it from falling into local optima. Select the vise as an example to verify the feasibility of the improved algorithm for solving the assembly sequence planning problem, and obtain the feasible assembly sequence with the lowest fitness.