In this paper, an intuitionistic fuzzy C-means clustering algorithms (IFCM) based on genetic algorithms is proposed. Compared with other methods based on various similarity matrixs, more objective results can be obtained by utilizing the optimal method to do clustering analysis. Firstly, the IFCM clustering method currently in use is discussed. By using this method, a local optimal value may be obtained as fuzzy C-means. The method proposed in this paper can overcome this drawback by combining that method with the GA method. In this process, the main problem is that the real number is changed into the intuitionistic fuzzy number. As a result, the clustering center is also changed from a real number to an intuitionistic fuzzy number. There may be some difficulty in handling with each clustering center, because the sum of membership, non-membership and uncertainty must be considered for each intuitionistic fuzzy number. In the GA program, after the cross operation, normalization is done for each clustering center to get the ideal results. At the end of this paper, an example is given and three methods are compared to illustrate the effectiveness of this method.