After more than 25 years of existence, evolutionary multi-objective optimization has become a mature discipline within evolutionary computation, producing an important flow of publications each year. This paper presents a brief overview of the main topics on which researchers in this area are currently working, as well as some discussion of the areas which, from the author's perspective, constitute promising research directions for the next few years. The topics discussed include algorithmic design, scalability, efficiency, hybridization, parameter control, theory and incorporation of user's preferences. The contents of this paper intends to provide a quick overview of the current state and challenges within evolutionary multi-objective optimization, and is intended to be useful for those interested in pursuing research in this area.