Design parameters of a flight control system are optimized by a probabilistic method. Simulated Annealing is applied for the optimization while the Downhill-Simplex method is added to generate new design vector candidates. The cost function to be minimized is chosen as the probability of violating the design criteria, and it is derived by Monte Carlo evaluation that incorporates various uncertainties. Thus, the designed system is robust against these uncertainties. The feasibility of the algorithm is demonstrated by designing a control system for a simplified model. The algorithm is compared both with the Downhill-Simplex method and the Genetic Algorithm. For the simple example, the results show that Simulated Annealing is more effective than the Downhill-Simplex method for parameter optimization, and it requires less computational time than the Genetic Algorithm. Furthermore, the algorithm is applied to the longitudinal flight control design of automatic landing system. It is demonstrated and verified that the algorithm is an efficient control design method.