Abstract: In-warehouse picking operations play a crucial role in automotive spare parts warehouse operations, directly impacting warehouse operation costs and efficiency. Taking D′s automotive spare parts warehouse as the research subject, and considering constraints such as picking time limits, employee picking capacity, and picking distances, a multi-objective optimization model incorporating maximum picking time and cumulative picking time was developed. The model aims to synergistically optimize task allocation and route planning for picking operations, using an adaptive large neighborhood search algorithm to derive optimal solutions. The results are as follows. 1) Compared to the pre-optimization state, the optimized system has reduced employees′ cumulative picking time by over 14%, shortened average picking paths by over 19%, and balanced employee working hours more effectively. This enables employees to complete more picking tasks with shorter routes, thereby significantly improving work efficiency. 2) In numerical experiments, 2 000 order line tasks can be quickly planned to obtain an optimal task allocation scheme and corresponding picking paths, achieving the objective of rapidly solving large-scale task allocation problems in a short time. The research findings not only provide guidance for improving picking efficiency in spare parts warehouses, but also offer valuable insights for optimizing picking operation efficiency in related industries.