To quickly realize the autofocus of a simple image and further improve the efficiency of automated part fabrication, a novel fast focusing algorithm was proposed according to the characters of images with single and clear backgrounds and foregrounds. Firstly, a series of part images including their position information were acquired. Then, all variance grey span values of target edges in each image were calculated automatically and the minimum span value of the clearest image was found quickly. Finally, the position information of the clearest image was feedbacked to a hardware driving system to complete the autofocus.An experiment on several parts with different shapes or materials was performed and obtained results were compared with that of the classic optimal focus function.The experimental results on those images added with salt-and-pepper noises demonstrate that the new algorithm is not only more sensitive but also has stronger unimodality and stronger anti-noise ability than those of classic optimum algorithms.Moreover,its computing speed is more than 30% faster than that of the fastest absolute gradient function. It concludes that the novel algorithm is robust and can be better used for a fast focus on simple scenes.