Clinical information systems store a large amount of data in medical databases. In the use of medical dataset for diagnosis, the patient’s information is selectively collected and interpreted based on previous knowledge for detecting the existence of disorders. Feature selection is important and necessary data pre-processing step in medical data classification process. In this work, we propose a wrapper method for feature subset selection based on a binary version of the Firefly Algorithm combined with the SVM classifier, which tries to reduce the initial size of medical data and to select a set of relevant features for enhance the classification accuracy of SVM. The proposed method is evaluated on some medical dataset and compared with some well-known classifiers. The computational experiments show that the proposed method with optimized SVM parameters provides competitive results and finds high quality solutions.