Research indicates that with the increase of objective dimensions, performances for most multi-objective evolutionary algorithms rapidly deteriorate, resulting in the population fails to converge and evenly distribute in PF. In view of the issue, this paper proposes an indicator selection and density estimation deletion-based many-objective evolutionary algorithm (MaOEA/IS-DED). In this algorithm, selection strategy based on Iε+ indicator and deletion mechanism based on shifted-based density estimation (SDE) are used to guide the population evolution. More specifically, the former is designed to find a pair of individuals with the minimum Iε+ indicator values, which denotes these selected individuals have the most similar search directions in space. The latter, taking into account the convergence and diversity of the population, compares these selected individuals and deletes the worse one. Experimental results demonstrate MaOEA/IS-DED can gain the highly competitive performance when dealing with many-objective optimization problems.