These days overhead crane is widely used in different industries such as automobile companies, harbor, navigation and also transportation of tools in storerooms. Most of models which is done through industrial dynamic systems include some vitiated parameter with noise and disturbance which overhead crane model is not also an exception. Disturbance in system can be due to its model or measuring tool. Kalman filter is a practical method in order to recognize the model and also filtration of disordered data. By the note of that overhead crane is a nonlinear model, asymmetric sigma-point Kalman filter improved by genetic algorithm (GA-ASKF) is intended to estimate system parameters. One of common ways in controlling overhead crane parameters is using controlling force, Bang-Bang. By the way, function of Bang-Bang controller depends on controlling force switched times. In this paper, beside using this controller, its switched times is found by using genetic algorithm for noisy system. The design aim is to achieve the target point in minimum time with minimum error. Also by considering Bang-Bang controller entrance part, the article is compared situation of the system in different mass relativeness. Simulation results shows improved performance of the GA-ASKF algorithm to determine the switching time of controller and also achieving the target point in minimum time.