Auto-focusing is one of the key technologies in the area of robot vision, digital imaging systems and precision op-tical instrument. With the continuous development of science and technology and improved application demands, it is more and more urgent to develop an auto-focusing with high precision, fast speed and good stability. While the existing auto-focusing techniques can’t meet the above requirements, a further study on auto-focusing makes a very important practical significance. The depth from defocus method and the depth from focus method are two typical passive auto-focusing methods of autofocus method based on digital image processing. The depth from defocus method is popularly used in depth estimation and scene reconstruction, which can measure the position of samples by just a few images. Therefore, the efficiency of the method is high. However, the accuracy of the depth from defocus method is relatively low because the small number of images is collected by the method. The depth from focus methods are based on the fact that the image formed by an optical system is focused at a partic-ular distance whereas objects at other distances are blurred or defocused. Very high accuracy can be achieved by depth from focus methods. In order to achieve efficient autofocusing, several commonly used search algorithms are studied, and a new low-computational search algorithm is presented, which combines the mountain-climb search strategy with the approximation function strategy to realize the hybrid search algorithm accurate and effi-cient autofocus. In this algorithm, the mountain-climb search strategy adopts the two-stage algorithm of rough and fine focusing stage. In the rough focusing stage, the large step distance takes into account the fastness of the algo-rithm, and the gray variance function is used to approach the focusing position quickly. In the fine focusing stage, the small step distance takes into account the sensitivity of the algorithm and the Laplacian function is used to locate the focusing position accurately. The algorithm narrows the focus interval by comparing three pictures and in the range uses the approximation function strategy to fit the best focus position. This method makes greatly improve the search accuracy. The experimental results indicate that this the algorithm can make the search accu-racy better than 1 μm. And the method only needs to capture 17 pictures, reducing the number of image acquisi-tion and evaluation. As a result, the time of the autofocus system is shortened and the search efficiency of the al-gorithm is improved.