The flight control parameter tuning of an aircraft is a tedious task even for experienced engineers. In this paper, a Modified Bacterial Foraging Optimization (MBFO) algorithm is developed to deal with control parameter tuning problems and explore the inner relationship between control parameters and the aircraft Flight Control System (FCS). In the proposed method, a serial of bacteria is first assigned to the searching space according to the quasi-Monte Carlo sampling method. Then, the whole colony is divided into several search groups according to the dynamic k-means clustering method. The bacteria are updated according to the nutrient variation in the environment and are always moving towards the areas with more food sources. Moreover, a health assessment strategy is adopted to eliminate bacteria with weak searching abilities and replace them with healthy bacteria to maintain the robust searching ability of the whole colony. Finally, a variety of feasible solutions is illustrated and comparative results are analyzed. A longitudinal FCS of the F/A-18 model designed for aircraft automatic landing is used as a test bed to carry out simulation studies to demonstrate the effectiveness of the proposed method.