During the last few years, there has been an increasing interest in using heuristic search algorithms based on natural selection (the so-called "evolutionary algorithms") for solving a wide variety of problems. As in any other discipline, research on evolutionary algorithms has become more specialized over the years, giving rise to a number of sub-disciplines. This talk deals with one of the emerging sub-disciplines that has become very popular due to its wide applicability: evolutionary multi-objective optimization (EMO). EMO refers to the use of evolutionary algorithms (or even other biologically-inspired heuristics) to solve problems with two or more (often conflicting) objectives. Unlike traditional (single-objective) problems, multi-objective optimization problems normally have more than one possible solution. Thus, traditional evolutionary algorithms (e.g., genetic algorithms) need to be modified in order to deal with such problems. This talk will provide a general overview of this field, including its historical origins, its most significant developments, some of its most important application areas and its current challenges.