In this paper, vector quantizer optimization is accomplished by a hybrid evolutionary method, which consists of a modified genetic algorithm (GA) with a local optimization module given by an accelerated version of the K-means algorithm. Simulation results regarding image compression based on VQ show that the codebooks optimized by the proposed method lead to reconstructed images with higher peak signal-to-noise ratio (PSNR) values and that the proposed method requires fewer GA generations (up to 40%) to achieve the best PSNR results produced by the conventional GA + standard K-means approach. The effect of increasing the number of iterations performed by the local optimization module within the proposed method is discussed.