A manufacturing service composition optimization method based on non-cooperative game theory is proposed to address the gap in specialized service composition optimization methods tailored for guide rollers in cloud manufacturing. This research encompasses analysis of the structure and production intricacies of guide rollers as well as consideration of the interests of both the manufacturing service demand side and the cloud manufacturing platform operator. The research commences by designing an attribute index system that serves as a foundation for service composition optimization. The key players in this scenario are the manufacturing service demand side and the cloud manufacturing service platform operator, with the objective of enhancing quality of service and flexibility. Based on the attribute index system, a non-cooperative game decision model is constructed to effectively optimize the manufacturing service composition for guide rollers within the cloud manufacturing environment. Furthermore, an algorithmic enhancement strategy rooted in NSGA-II is proposed to tackle this model. This optimization strategy aims to refine the NSGA-II algorithm by optimizing the initial population and augmenting the jump operation. Experimental results show the effectiveness and superiority of this approach and the enhancement strategy in optimizing the service composition specific to guide roller manufacturing.