A differential evolution algorithm based on low-density individuals in the neighborhood is proposed to solve MultiModal Optimization Problems (MMOPs). In each generation, the algorithm first relies on density peak clustering to find the density of each individual and then take the lower-density individuals in the neighborhood of the current individual as a base vector of the mutation operator. As the population evolves, the algorithm will automatically transform from the exploration stage to the convergence stage, thereby balancing its exploration and convergence capabilities. The proposed algorithm is applied to the CEC2013 multimodal benchmark function for simulation experiments. Results demonstrate that the algorithm has obvious advantages over other multimodal optimization algorithms based on differential evolution in evaluating the peak ratios and stability of indexes, and the advantage is more distinct with the increasing dimensionality and complexity of the test function. It behaves better than many existing multimodal optimization algorithms based on differential evolution.