Abstract: In the foundry workshop of a household enterprise, sand core processing and casting processing are key links, and the two are usually interrelated and have a chronological sequence relationship: sand core processing needs to be completed before casting, and casting depends on the processing progress of the previous sand core. In actual production, due to the lack of coordination between the two operation plans, it often leads to the delay of one party directly affecting the progress of the other party, which leads to the delay of the entire order, and at the same time, there are mold constraints in the production process. Based on the above problems, this paper constructs a joint scheduling optimization model with the goal of minimizing the total order delay time, aiming to solve the job scheduling problem in the casting shop through an improved grey wolf optimization algorithm, and proposes a knowledge-based evolutionary search operator to enhance the global search capability and improve the accuracy and efficiency of the solution. Based on the actual order and process structure of the enterprise, several benchmark examples are used to optimize the parameters combined with orthogonal test method. The experimental results show that the proposed algorithm is superior to the traditional scheduling algorithm in solving efficiency and optimizing quality. In terms of reducing the order delay time, the improved algorithm has significant advantages compared with the comparison algorithm, and the application effect is good in actual production, which can effectively reduce the uncertainty in the process of operation and improve the precision and flexibility of production scheduling.