The purpose of this study is to develop a computerized scheme that allows discrimination between benign and malignant clustered microcalcifications, thus aiding radiologists to better interpret mammograms. The fuzzy-logic based genetic algorithm (GA-Fuzzy) that we propose is applied for the classification of mammographic microcalcifications. As a preprocess, microcalcification regions are extracted from images using mathematical morphology and other means. Then, four feature values including the number, area, circularity and minimum distance of microcalcifications are calculated for classification using GA-Fuzzy. In the GA-Fuzzy process, Gaussian-distributed membership functions are determined from the means and standard deviations of the feature values calculated from training images. Subsequently, the genetic algorithm optimizes the membership functions, and training is completed. Next, the feature values calculated from unknown images are input into GA-Fuzzy system to classify the image. Our scheme was tested utilizing the database of the Mammographic Image Analysis Society (MIAS), which contains 13 benign and 12 malignant microcalcification cases. Of the images, there are ten of each benign and malignant cases respectively used for training. The remaining five cases were used for classification as unknown images. Various combinations of sets are employed to obtain the results. The average accuracy of the GA-Fuzzy technique was approximately 81% (sensitivity, 85%; specificity, 77%). These results show that our scheme can be regarded as a useful technique to classify microcalcification cases.