The textural-feature-based segmentation methods were widely applied to the segmentation problems of ultrasonic images. However the manual selection of textural features in the previous approaches not only makes these segmentation methods inadaptable but could lead to the results with bias. Herein we propose an auto-feature-selection algorithm to solve the problems. This algorithm includes three steps: The feature library composed of 32 textural features was established at first. The genetic algorithm was then used to auto-select the features and give each of them different weight according to their importance. The fitness of each gene was evaluated by five factors including region dissimilarity, number of edge points, edge fragmentation, edge thickness, and curvature. Finally, K-means process classified the image into 3 different tissues using the selected features with different weights. The segmentation outcomes of various ultrasonic images by this auto-feature selection algorithm have shown better correspondence with human comprehension in comparison with the results of previous works. In addition, it provides a more adaptive way to adjust the weight of the features used for clustering process and therefore to avoid takeover by the big-value features. This problem has been paid little attention in the traditional K-means process in which all the features have the same weight.