Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the so-called multi-objective optimization problems). This area, known as EMOO (Evolutionary Multi-Objective Optimization) has had an important growth in the last 15 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this talk, we will review some of the research topics on evolutionary multi-objective optimization that are currently attracting a lot of interest (e.g., handling many objectives, hybridization, indicator-based selection, use of surrogates, etc.) and which represent good opportunities for doing research. Some of the challenges currently faced by this discipline will also be delineated.