This chapter focuses on the use of genetic algorithms (GAs) in the design of FLC. An approach of adopting genetic algorithm search is adopted to determine optimal FLC scaling factors. The approach is then extended by adoption of neural network learning of the scaling factors leading to a neuro-fuzzy control method. This is further combined with genetic algorithm for optimisation of shape of activation function of the neural network. Case study experimental investigation exercises are presented demonstrating the performances of the developed paradigms in the control of a single-link flexible manipulator system.