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In this paper, the self organization properties of genetic algorithms are employed to tackle the problem of feature selection and extraction in ultrasound images, which can facilitate early disease detection and diagnosis. Accurately identifying the aberrant features at a particular location of clinical ultrasound images is important to find the possibly damaged tissues. Unfortunately, it is difficult to exactly detect the regions of interest (ROIs) from relatively low quality of clinical ultrasound images. The presented evolutionary optimization algorithm presents a novel approach to building features for automatic liver cirrhosis diagnosis using a genetic algorithm. The extracted features provide several advantages over other feature extraction techniques which include: automatically construct feature set and tune their parameters, ability to integrate multiple feature sets to improve the diagnosis accuracy, and ability to find local ROIs and integrate their local features into effective global features. As compared with past approaches, we span a new way to unify the processing steps in a clinical application using the evolutionary optimization algorithms for ultrasound images. Experimental results show the effectiveness of the proposed method.