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This paper demonstrates the control parameter optimization using a hybrid Differential Evolution (DE) - Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for permanent-magnet brushless DC (BLDC) motor drive system ensuring deadbeat response. The parameter settings are further used in adaptive current and speed controllers to attain the objective in a BLDC drive system. The parameters of both inner-loop and outer-loop PI controllers, which vary with the operating conditions of the system, are adapted in order to maintain deadbeat response for current and speed. The ANFIS is modeled using the evenly distributed operating points selected within preset regions of system loading. The optimized data from DE are used to train the ANFIS that could deduce the controller parameters at any other loading condition within the same region of operation.