Abstract: From the aspect of solution space,the role of crossover operators is analysed firstly. The essence of crossover operators is that it can choose values at random from the solution space included father individuals. So, it is not absolute that the performance of offspring individuals is better than that of father individuals after the father individuals crossed. And it is very easy to bring the aimless search. An improved genetic algorithm based on oriented crossover is proposed. It can make the offspring individuals evolve towards the target value by optimizing their crossover positions and the evolving probability is very large. The simulation results show the algorithm can improve greatly the efficiency and precision in finding the optimum value.