The key to surgical planning for breast conservation is tumor localization. An accurate localization of the breast tumor is essential to guide the surgeon to the lesion, and ensure its correct and adequate removal with satisfactory excision margins. Current breast tumor localization techniques are invasive and often result in a cosmetic disfigurement. In this paper, we use the ultrawide band radar-based microwave breast imaging technique to non-invasively localize (impalpable) tumors in the breast. We consider four clinically important lesion features: location, size, depth and spatial orientation within the breast. A comparison of the energy of the received signal from healthy and cancerous breasts exhibits some significant differences in some frequency bands. We, therefore, use the energy spectrum of the receiving antenna signal decomposed by wavelet transform as the input to a genetic neural network (GNN) classifier. Furthermore, for improved efficiency, we optimize the structure of the GNN for optimum initial weights and number of hidden nodes. We use CST Microwave Studio to simulate benign and malignant breast conditions, and generate a data set of 1024 cancer cases with various tumor location, size, depth and spatial orientation within the breast. Our results show that the proposed algorithm gives accurate localization of the breast lesion, and possesses a high sensitivity to small tumor sizes. Additionally, it can accurately detect and classify multiple tumors.