Abstract: A Guided Self-adaptive Evolutionary Genetic Algorithm (GSEGA) is proposed. The principle of good point set is used to generate the initial population. Based on the elitist preserved method, a way of parallel crossing and mutation with population-segmentation is offered, in which a son population among the segmented population is randomly generated. In addition, a guided self-adaptive mutation strategy based on the statistics of the more excellent individualities is adopted on the other part of the son population to speed up the evolution. Through the use of the homogeneous finite Markov chain model, the global convergence and high searching speed of the GSEGA is proved. The experimental results show that the GSEGA presents a higher speed and precision in comparison with the other Genetic Algorithms (GAs).