Human and animal locomotion are controlled by complex neural circuits, which can also serve as inspiration for designing locomotion controllers for dynamic locomotion in legged robots. We develop a locomotion controller model including a central pattern generator (CPGs) and a muscle reflex based on the forelimb and hindlimb structures of a cat. In this paper, we focus on modeling the muscle reflex and its optimization. This muscle reflex model regulates ground force afferents in each limb. There are two phases in each step performed by this model, the swing and stance phases. The muscle during swing phase is activated by a pattern formation signal from the CPG. During stance phase, the muscle is automatically controlled by the moving speed. We utilize a multi-objective evolutionary algorithm to optimize parameters of the model. We use the proposed model to control a cat-like robot in simulations using Open Dynamics Engine. Results show that the simulated robot is able to move at different speeds by modulating simple stimulation signals to the CPG without needing to modify muscle and reflex parameters.