Robotics systems are, in general, complex mechanisms. Modeling and simulations are useful tools for analyzing various parameters in order to predict the performances of these systems. Designers can easily predict the system’s performances based on its input parameters. Hence, through modeling and simulations, designers can arrive at some feasible solutions for a given set of system parameters without carrying out any real experiment. Thus, there is a saving of time and materials resources. In this book, evolutionary algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to solve the optimization problems related to two robotic systems. At first, an attempt is made to design and develop an optimal structure and controller for a 2-DOF serial manipulator tracking a given trajectory. Secondly, optimal gait planning problems of a 7-DOF biped robot have been tackled in both single and double support phases separately. These gait planning problems have been posed as multi-objective optimization ones and Pareto-optimal fronts of solutions have been obtained.