The Pareto envelope-based selection algorithm II (PESA-II) is a classic evolutionary multiobjective optimization (EMO) algorithm that has been widely applied in many fields. One attractive characteristic of PESA-II is its grid-based fitness assignment strategy in environmental selection. In this paper, we propose an improved version of PESA-II, called IPESA-II. By introducing three improvements in environmental selection, the proposed algorithm attempts to enhance PESA-II in three aspects regarding the performance: convergence, uniformity, and extensity. From a series of experiments on two sets of well-known test problems, IPESA-II is found to significantly outperform PESA-II, and also be very competitive against five other representative EMO algorithms.