In autonomous excavation, design and optimization of the unmanned cable shovels (UCS) are important issues in the full life cycle of the equipment. However, the design of physical structure and control system of the UCS are performed at different stages, which makes it difficult for traditional sequential optimization strategy to generate global optimal solution. To enhance the working performance of the UCS, in this article, a multistage multiobjective (MSMO) co-design optimization strategy is proposed to perform global optimization considering excavation and loading processes by simultaneous optimization for the structure and control parameters of UCS. Under this framework, first, a point-to-point motion model based on 4-5-4 piecewise polynomial is proposed to describe the motion trajectory, and the dynamical model of the working device is established to predict the energy consumption in the working process. Then, the physical and geometric constraints in practical working are analyzed, and a multiobjective optimization model considering excavation and loading processes is established to improve mining efficiency and reduce energy consumption in unmanned excavation scenarios. Finally, the structural and control parameters are optimized synchronously to generate optimal physical structure, excavation, and loading trajectory. Numerical results show that the proposed MSMO co-design method can further improve the operational performance of UCS compared with the traditional optimization strategy.