Blind image restoration is performed by a history based Genetic Algorithm (GA) approach which is presented in this research. The motivation of the proposed method is that GA has evolutionary process: it will pair the chromosomes to create the optimized children preserve the best restoration quality. Fewer effective solutions are discarded and only the most optimized values make it to the subsequent processing. This iterative refinement goes on and on in multiple generations in order to improve the quality of an image progressively. Due to the lack of prior knowledge about the Point Spread Function (PSF), blind image deblurring is a key challenge, which makes the restoration process highly complex. This challenge is effectively addressed by the proposed algorithm, which successively refines optimized values by iteratively selecting optimized values, and achieves better deblurring result. An approach is developed and successfully implemented and verified to restore degraded images through the automatic detection and application of appropriate corrections and to extract accurate information from such images.