Genetic algorithm is often applied to optimization problems in industrial production, but facing non-linear, multi-extreme, and multi-variable problems, it is easy to fall into the local optimal range in the early optimization process. By adding catastrophe operation to the traditional genetic algorithm, this study reduces the common “premature” phenomenon of genetic algorithm, and sets the adaptive change of genetic operation with the change of iteration times of catastrophe operation, which enhances the optimization ability of the later period of the algorithm. The algorithm is tested with the actual production data of a steel enterprise in Hebei Province. The experimental results show that the algorithm can effectively reduce the cost of raw materials, reduce the occurrence of premature phenomena, improve the overall search ability of the algorithm, and can play a significant role in the optimization of industrial production under the premise of guaranteeing the performance and quality of sinter.