To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.