In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. The displacement change the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling factors) of each fuzzy controller using HFCGA. The inner controller controls the position of lever arm which corresponds to the position angle of a servo motor and the outer controller decides the set-point value of the inner controller. HFCGA is a kind of parallel genetic algorithms(PGAs), and helps alleviate the premature convergence being generated in conventional genetic algorithms (GAs). For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.