Traditional camera imaging suffers from insufficient preservation of high-frequency information, inaccurate solution of encoding exposure codewords, and difficulty in estimating blur kernels. Toward solving these problems, this study focuses on a codeword searching method for coded exposure cameras and proposes an intelligent optimized cyclic search strategy based on a memetic algorithm framework. A mutation crossover operator is used in differential evolution to obtain a global solution; subsequently, a taboo search is performed to conduct a local search on the global solution, thereby iteratively searching to obtain an optimal codeword sequence. A loss function suitable for encoding exposure image restoration is designed, and an end-to-end blind deconvolution kernel generative adversarial network is used to compare the performances of different codeword acquisition methods for blurry image restoration. Experimental results show that the proposed intelligent optimization algorithm can solve the codeword sequence more accurately and with better robustness than the other methods. When using the same network for blurred image restoration, the proposed algorithm yields superior restoration results compared with the existing methods from subjective and objective perspectives. Thus, the proposed method has a high engineering application value for enhancing motion blur restoration.