<p>In the robotics community, multiple robots can be connected to form a serially connected robot, enabling adaptation to diverse environments and task requirements. However, the significantly large design space, high-dimensional observations, and complex actions pose significant challenges in designing serially connected robots with well-adapted morphologies. To address these problems, we propose a multi-objective optimization framework that first optimizes a single robot for easy tasks, then replicates its structure to construct a serially connected robot for hard tasks. For generalizing across scale variations of a single morphology, we propose a novel control method based on information sharing to learn a two-robot controller with combinatorial generalization capabilities. The two-robot controller can zero-shot generalize various sizes of serially connected robots, ensuring good motion coordination while significantly reducing the cost of control optimization. Experimental results show that the proposed algorithm outperforms the other design algorithm in most tasks. The serially connected robots can achieve reliable movement. By combining information sharing with the two-robot controller, agile motion can be achieved under complex terrain conditions. Compared to baseline algorithms, the proposed algorithm achieves superior performance on all hard tasks, especially with large-scale serially connected robots.</p>