A four-step hybrid method for the design of neuro-fuzzy motion controllers is presented. The design method starts from a preliminary known good control strategy used as learning data. The aim of the method is to find a controller that reproduces as close as possible the good control strategy and ensures the accomplishment of the required motion for the controlled mechanism. An Improved Simple Genetic Algorithm and the Weighted Errors Balance Algorithm were combined with the Global Learning principle within the first two steps of the method in order to design a Takagi-Sugeno type fuzzy controller. The evaluation of the robots' evolution is then used in order to simplify the controller's structure by reducing the number of rules within the rulebase. Finally, the parameters of a feed-forward type neural network structure, that embeds the simplified Takagi-Sugeno fuzzy controller, are derived. As an example, the paper presents the simulated evolution of a brachiation mobile robot (BMR) under the control of the neuro-fuzzy motion controller designed using the proposed method.