Many-objective optimization problems take challenges to multi-objective evolutionary algorithms. A number of non-dominated solutions in population cause a difficult selection to-wards the Pareto front. To tackle this issue, a series of indicator-based multi-objective evolutionary algorithms (MOEAs) have been proposed to guide the evolution progress and shown promising performance. This paper proposes an indicator-based many-objective evolutionary algorithm called ε-indicator-based shuffled frog leaping algorithm (ε-MaOSFLA), which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front. Compared with four state-of-the-art MOEAs on several standard test problems with up to 50 objectives, the experimental results show thatε-MaOSFLA outper-forms the competitors.