The use of general purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach and examines the performance on both Tesla and Fermi architecture GPUs and identifies the best way to determine the GPU parameters for the proposed method.