Fundamental matrix, the key to many problems of computer vision, encapsulates all the information on camera motion and internal parameters available from image feature correspondences between two views. In this paper, we present a new approach based on genetic algorithm to estimate fundamental matrix, which uses a minimum number of corresponding point (seven points) as a subset for fundamental matrix estimation and can eventually find the near-optimal solution without the need of initial guesses. Results from our extensive study using both synthetic data and real images demonstrate the excellent performance of the proposed technique in terms of robust, accuracy and convergence.