As a kind of swarm optimization algorithm with good performance, the artificial bee colony (ABC) algorithm is presented in recent years. However, it exist some disadvantages, such as the convergence speed is not fast enough, easy to fall into local optimum and etc. In order to solve this problem, an improved algorithm called DCABC is presented. In this algorithm, the opposition-based learning method is employed when producing the initial population, the divide-and-conquer strategy is adopted to greed update food resources. After employed bees releasing updated food source information, onlookers choose optimal resource based on the divide-and-conquer strategy. Experiments are conducted on a set of 6 benchmark functions, and the results show that DCABC has better performance than several other ABC-based algorithms, especially on the accelerating convergence and the global search ability and efficiency.