In image matching research, how to ensure that best match’s accuracy of the premise and a significant reduction in the amount of computing is the focus of concern by researchers. Search strategy to find the best match location of the image matching process to determine the amount of computing of image matching, in the existing image matching method are used to traverse search strategy, it is difficult to reduce the amount of computing. This is a common defect of the existing image matching algorithm. Cultural Algorithms are a class of computational models derived from observing the cultural evolution process in nature, compared with genetic algorithm the cultural algorithms have high convergence speed. In this paper, compared with genetic algorithm on the Benchmarks function, the results show that the new algorithm is efficient, we also use the new algorithm to solve image matching problem and the experiment results show the new algorithm is effective for this problem.