The microarray technology plays an important role of clinical oncology field. The patient can be diagnosed a symptom about cancer through microarray data. Currently, to solve classification of microarray data is still a wild open issue. Existing methods may have a good performance, but need to spend much time to analyze microarray data, such as SVM. In this thesis, we propose a novel GA-Fuzzy-based voting mechanism to find genes which affect the symptom to better diagnose patient. The proposed algorithm can blur the boundary between classes to handle the ambiguous regions. In order to simulate the gene selection mechanism, we proposed upper bound £\-Cut and lower bound £\-Cut in voting mechanism. Two groups of data collected from the literature are used to test the performance of the proposed algorithm. In the first group of dataset, experimental results show that the accuracies of five datasets using the proposed algorithm are better than those methods proposed by Pochet et al. But, there are the four datasets which the accuracies using the proposed algorithm are a little bit worse than the methods proposed by Pochet et al. For the second group of dataset, the accuracies of seven datasets using the proposed algorithm are better than KerNN proposed by Xiong and Chen. But, there are four datasets which the accuracies using the proposed algorithm are worse than KerNN proposed by Xiong and Chen. Nevertheless, experimental results show that the proposed algorithm performs the best for multi-class data.