Abstract: The inner-guided hydraulic cylinder is a key component of the aluminum alloy casting machine, and the crane plays a vital role in the production process of this hydraulic cylinder. Therefore, a flexible job shop scheduling model for the coordination of material processing, handling and loading/unloading is established, and an improved grey wolf optimizer is proposed to solve the multi-objective optimization problem including completion time, equipment energy consumption and workload variance of cranes. The algorithm focuses on crane scheduling rules in the decoding stage, and utilizes the theory of good point sets along with a hybrid strategy to generate an initial population that balances diversity and quality. Additionally, an adaptive hunting weight coefficient and a nonlinear convergence factor are designed, and a velocity-assisted term is introduced to enhance the algorithm's optimization capability, while high-quality solutions are preserved through external archive. Four neighborhood search structures are proposed to strengthen local search. Compared with other algorithms by various evaluation indexes, the improved grey wolf optimizer demonstrates its effectiveness in solving the cooperative scheduling problem considering crane operation constraints.