Motion planning and tracking control are two fundamental issues in mobile robotics. The path planning is to generate a collision-free trajectory for a mobile robot to move from an initial configuration to a goal configuration. In the first part of this thesis, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The main feature of this algorithm is that the chromosome has a variable length, which is more suitable than using fixed-length chromosomes in a dynamic environment. The proposed algorithm is capable of generating a near-optimal robot path in both static and moving-obstacle environments with obstacle avoidance. The tracking control is to generate control velocities to drive the mobile robot to follow a desired path. In the second part of this thesis, genetic algorithms are applied to optimize the performance of the backstepping and sliding mode tracking controllers for a point mobile robot by tuning the model parameters. The proposed genetic algorithm based backstepping controller can guarantee the system stability and convergence of tracking error to zero. It eliminates the oscillation at the initial phase in both the linear and angular velocities. In the proposed genetic algorithm based sliding mode controller, the optimal parameters are also obtained using a genetic algorithm. It can remove the chattering in the initial phase. The system is asymptotically stable and the error converges to zero. Finally, the proposed path planner and tracking controller are integrated into one system for both path planning and tracking control of a mobile robot. The effectiveness is demonstrated by simulation studies.