Microarray data has been widely used to predict different disease condition. But the problem has been the high dimensionality of microarray data, because of very few samples compared to a huge number of genes. To tackle this necessity we have developed EVOL Optimer (Evolutionary Optimization). In our method we used both filter and wrapper based approach for gene selection. The original subsets are ranked by using the filter based ReliefF ranking method then the top ranked features are optimized using a wrapper based genetic algorithm (GA) method. The selected gene subsets are applied to different classifiers with Leave-One-Out Cross Validation (LOOCV) and the results show that the proposed approaches have high classification accuracy on different dataset. The tool is readily available at the following URL: (http://faculty.amrita.ac.in/~ashavijayan/evol/).