The accuracy of a multibody model to predict the behavior of a real physical system depends heavily on the correct choice of model parameters. Identifying unknown system parameters that cannot be computed or measured directly is usually time-consuming and costly. If measurement data is available for the physical system, the parameters in the corresponding mathematical model can be determined by minimizing the error between the model response and the measurement data using optimization methods. While there is a wide range of optimization methods available, genetic optimization is a more generic approach for finding optimal solutions to complex engineering problems. So far, however, there is no general approach on how to use genetic optimization to determine unknown system parameters automatically—which is, however, of great importance when dealing with real flexible multibody systems. In this paper, we present a methodology to automatically determine several unknown system parameters of a complex flexible multibody system using genetic optimization. The proposed methodology is demonstrated using a small-scale test problem and a real flexible rotor excited with impacts. Experiments were performed on the physical rotor to obtain measurement data which is used to identify bearing and support stiffness and damping parameters as well as the impact force.