To address the path planning requirements for automated visual inspection of workpieces with complex surfaces—such as automotive steering wheels—conventional decoupled approaches that first select viewpoints and subsequently plan paths are prone to local optima and struggle to simultaneously achieve high coverage and computational efficiency. To overcome this limitation, a coverage path planning method featuring joint optimization of viewpoints and trajectories is proposed. First, the axis-aligned bounding box of the target point cloud model is spatially subdivided, and candidate viewpoint positions are generated by applying random offsets to the centroids of the resulting subregions. Viewpoint orientations are then determined through sampling in spherical coordinates, yielding a redundant set of candidate viewpoints. Second, a frustum-based coverage evaluation model is established, incorporating constraints on depth of field, field of view, surface visibility, and self-occlusion to quantitatively assess the effective coverage capability of each candidate viewpoint. Finally, an enhanced hybrid grey wolf optimizer-whale optimization algorithm (IGWO-WOA) is introduced, integrating chaotic map-based initialization, opposition-based learning, a dynamic convergence factor, and the spiral hunting mechanism from the whale optimization algorithm to enable multi-objective co-optimization of viewpoint selection and visiting sequence. Experimental results on two complex steering wheel models demonstrate that, compared with conventional swarm intelligence algorithms, the proposed method reduces path length by 20.6% and 11.5%, achieves coverage rates of 99.7% and 99.86%, respectively, and ensures collision-free execution throughout the inspection process, thereby delivering significantly superior trajectory quality.