In the realm of modern enterprise production, maintaining a specific non—conformance rate during the procurement of spare parts is a crucial yet challenging task. This article delves deep into effective methods for quality inspections of spare parts, semi—finished products, and finished goods. The non—conformance of spare parts can lead to a series of problems, such as increased production costs due to rework or product recalls, and a decline in customer satisfaction. To tackle these issues, a genetic algorithm model is developed. This model serves as a powerful tool in optimizing production decisions. It encodes various decision—making variables in the production process, like whether to conduct quality tests at different stages and whether to disassemble defective products, into chromosomes. By setting the total profit as the fitness function, the model can simulate different production scenarios. The key advantages of this model are as follows. First, it takes into account the trade—off between product quality and cost. By optimizing the inspection and disassembly strategies at each production stage, it ensures that product quality is maintained at a high level while minimizing costs, thereby maximizing profits. Second, it can enhance customer satisfaction. High—quality products with fewer defects mean better user experiences, which directly contribute to improved customer satisfaction. Third, through sensitivity analysis, the model has proven to be robust under different defect rate conditions, making it highly applicable in real—world production environments. Overall, this genetic algorithm—based approach provides a comprehensive and practical solution for enterprises to balance quality control, cost—effectiveness, and customer satisfaction in their production processes.