-This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller. On the other hand, due to the existence of nonlinearity, it is usually difficult to conduct theoretical analysis to explain why fuzzy PID can achieve better performance. From the theoretical and practical points of view, it is important to explore the essential nonlinear control properties of fuzzy PID and find out appropriate design methods which will assist the control engineers to confidently utilize the nonlinearity of the fuzzy PID controllers to improve the closed-loop performance.There are many design factors determining its structure in a fuzzy logic controller such as membership functions, input space partition of fuzzy rules, various types of fuzzy inference mechanisms, defuzzification schemes, etc. They may appear either highly nonlinear or approximately linear. Nevertheless, to perform proportional, integral and derivative control modes,