Developed in recent years, artificial bee colony (ABC) algorithm is a relatively new global optimization algorithm that has been successfully used to solve various real-world optimization problems. However, in the algorithm, including its improved versions, the scout bee usually employs the random initialization method to generate a new food source. Although this method is relatively straightforward, it tends to result in the loss of the scout bee's search experience. Based on the intrinsic mechanism of ABC's search process, this paper proposes a new scheme that employs the orthogonal experimental design (OED) to generate a new food source for the scout bee so that the scout bee can preserve useful information of the abandoned food source and the global optimal solution in different dimensions simultaneously, and therefore enhancing the search efficiency of ABC. A series of experiments on the 16 well-known benchmark functions has been conducted with the experimental results showing the following advantages of the presented approach: 1) it can significantly improve the solution accuracy and convergence speed of ABC almost without increasing the running time; 2) it has better performance than other three typical mutation methods; and 3) it can be used as a general framework to enhance the performance of other improved ABCs with good applicability.