Breast Cancer (BC) is considered as the most implacable malignancy and the leading cause of mortality among women in general and in Saudi Arabia specially. Most of the previous work in Saudi Arabia on this subject was on epidemiology, knowledge of (BC) and practice of breast self-examination (BSE), etiological factors, metastases and rate of survival. Early detection and diagnosis of Breast Cancer (BC) is an important, real-world medical problem. In this paper, we propose a soft computing methodology to build a Breast Cancer (BC) diagnosis system with high capabilities as described by Andres et al.  but on the Saudi Arabian breast cancer dataset and using a simplified fitness function. We focus on combining fuzzy concepts and genetic algorithms so as to automatically produce diagnostic systems to support and assist the expert to understand and evaluate its results with high classification performance.