Differential  evolution  (DE)  has  been  used  to solve real-parameter optimization problems with nonlinear and multimodal functions for more than a decade of years. However, it is pointed out that this classical DE harbors restricted   efficiency   and   limited   local   search   ability. Inspired by that gradient-based algorithms have powerful local search ability, we propose a new algorithm, which is diversity-maintained  DE  based  on  gradient  local  search (namely,  DMGBDE),  by incorporating  approximate  gradient-based  algorithms  into  the  DE  search  while  maintaining   the   diversity   of   the   population.   The   primary novelties of the proposed DMGBDE are the following: (1) the  gradient-based  algorithm  is  embedded  into  DE  in  a different manner and (2) a diversity-maintained mutation is introduced to slow down the learning procedure from the searched  best  individual.  We  conduct  numerical  experiments with a number of benchmark problems to measure the  performance  of  the  proposed  DMGBDE.  Simulation results  show  that  the  proposed  DMGBDE  outperforms classical DE and variant without gradient local search or diversity-based   mutation.   Moreover,   comparison   with some other recently reported approaches indicates that our proposed DMGBDE is rather competitive.