With the continuous speed increase of high-speed trains, higher performance requirements are put forward for the braking technology and braking device. In order to improve productivity and overall competitiveness, manufacturing companies are actively researching new manufacturing technologies and production methods, and shop floor scheduling is one of the core components of this problem. This paper mainly studies the high-speed train composite workshop planning and scheduling optimization method. This experiment adopts B/S-based architecture mode. The prototype system is developed with Microsoft’s integrated development platform Visual Studio 2013, and Microsoft’s SQL Server 2008 is used as the background database management system. The experiment mainly uses the white box test method; the test content mainly includes module interface test, module local data structure test, and module boundary test. The interface parameters of each module are checked, and the boundary values of some functions are also analyzed and tested. According to the results, the planning management personnel revise the priority order again until all the molds meet the requirements of delivery and constraints. If the scheduled results do not meet the requirements, the methods such as those compressing the lead time, those urging the casting to be in place in advance, single process outsourcing, and overtime shall be considered. In this paper, a layered coding strategy is adopted. The first layer of coding represents the batch processing sequence. The second layer of coding determines to which process the corresponding batch belongs to. Each layer of coding is divided into different machine segments to represent the batch processing sequence on different machines. When the production process needs to switch orders, it can know which equipment parameters need to be adjusted in advance, which can effectively avoid the wrong operation caused by temporary adjustment of production parameters, reduce the order switching time, and improve the utilization rate of the production line. The data show that, compared with the artificial experience method and the priority rule method, the order production cycle after genetic optimization is reduced by 7.34% and 8.98%, respectively. The results show that the workshop scheduling optimization can help enterprises save stamping scheduling time, reduce production costs, and improve the rationality of scheduling.

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