Aiming at the demerits of artificial bee colony algorithm (ABC) such as lower optimization precision, slower convergence speed, and easy to be attracted by local optimal solution, an enhanced artificial bee colony algorithm with Lévy flight and opposition-based learning strategy (OBL) which is called ELOABC is proposed. Firstly, in the phase of employed bees and onlooker bees, the Lévy flight strategy is employed to improve the newly generated solution. It is because Lévy flight has random step length that it can improve the global optimization ability of the ABC algorithm. Secondly, in the scout bee’s phase, mutation solutions are generated according to the positions of the stagnation solution and the current optimal solution, and then the better solutions of mutation solutions and their opposite solutions which are generated in combination with the OBL strategy are retained to improve the solution accuracy of the algorithm. Finally, 15 well-known benchmark functions are utilized to validate the performance of the proposed ELOABC algorithm. The experimental results show that the proposed ELOABC algorithm outperforms other competitors evidently on optimization performance.