To address the low production efficiency and insufficient cross-enterprise collaboration in wallboard outsourcing for coating equipment manufacturing, this study proposes a wallboard manufacturing service composition optimization method based on an improved NSGA-III algorithm. First, the service composition optimization problem in wallboard manufacturing is analyzed, and a mathematical model for wallboard outsourcing service composition optimization is established. Next, an improved NSGA-III method integrating Latin hypercube sampling, greedy local search, and Lévy flight-based global search strategies is proposed, alongside a similarity measurement method for the objective-space structure based on statistical features and distribution differences, which is used to select benchmark functions that closely match the characteristics of actual business data. Finally, comparative experiments using benchmark functions and real-world business data are conducted to verify the effectiveness and superiority of the proposed method in solving service composition optimization problems. The experimental results demonstrate that the proposed method achieves excellent performance in both solution quality and computational efficiency, significantly improving the utilization of wallboard outsourcing manufacturing resources across enterprises and facilitating efficient business process flows.