To address the technical bottlenecks of low path planning efficiency and insufficient point cloud coverage in the automated 3D scanning of complex structural components, this study proposes an offline method for the generation and optimization of scanning paths based on CAD models. Discrete sampling of the model’s surface is achieved through the construction of an oriented bounding box (OBB) and a linear object–triangular mesh intersection algorithm, thereby obtaining a discrete point set of the model. Incorporating a standard vector analysis of the discrete points and the kinematic constraints of the scanning system, a scanner pose parameter calculation model is established. An improved nearest neighbor search algorithm is employed to generate a globally optimized scanning path, and an adaptive B-spline interpolation algorithm is applied to path smoothing. A joint MATLAB (R2023b)—RobotStudio (6.08) simulation platform is developed to facilitate the entire process, from model pre-processing and path planning to path verification. The experimental results demonstrate that compared with the traditional manual teaching methods, the proposed approach achieves a 25.4% improvement in scanning efficiency and an 18.6% increase in point cloud coverage when measuring typical complex structural components. This study offers an intelligent solution for the efficient and accurate measurement of large-scale complex parts and holds significant potential for broad engineering applications.