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Genetic algorithm has become a suitable searching or optimization tool for solving many complex problems comparing with the traditional search techniques. Genetic algorithm contains many manipulations to speed up and improve the genetic algorithm performance; one of these manipulations is the selection operations. The selection refers to select the best individual in the population and make it as parent in the next generation; the worst individual may be not select always. This process is done by using many different selection schemes. Three problems, Traveling Salesman Problem, Knapsack Problem and Solving Instantaneous Linear Algebraic Equation Problem are solved in this thesis by genetic algorithms with six different selection strategies, these schemes, the implementation, discussion of their effects on the performance of genetic algorithm and comparison between them as well as with the other works which are illustrated in this thesis. Finally, it is found that the uses of selection mechanisms are important to make genetic algorithm less susceptible to premature convergence and speed up the search process of finding the optimal solution.