Many-Objective optimization refers to optimizing the multi-objective optimization problems (MOPs) where the number of objectives is more than three. Most classical multi-objective evolutionary algorithms (MOEAs) use the Pareto dominance relation to guide the search and thus are hard to perform well in many-objective optimization problems. In this paper, a multi-objective evolutionary algorithm based on information separation (ISEA) is proposed. ISEA rotates the original coordinate system in the objective space, and makes the first axis parallel to the vector (1,1,…,1)^T. The first member of the new coordinate is defined as convergence information, and the remaining members are defined as diversity information. Moreover, a neighborhood penalty mechanism based on layered selection is adopted using the information of the neighborhood shape made of two hyper-cones to maintain the diversity of individuals. The first hyper-cone is used to cover neighbors, and the second one to cover extreme individual whose convergence performs significantly worse than others. Additionally, after an individual is selected into the archive set, its neighbors are punished into an inferior layer. From comparative experiments with other representative MOEAs, including NNIA, e-MOEA, MSOPS, AR+DMO, and IBEA, the proposed algorithm is found to be successful in finding well-converged and well-distributed solution set.