As the name suggests, multi-objective optimization involves optimizing a number of objectives simultaneously. The problem becomes challenging when the objectives are of conflict to each other, that is, the optimal solution of an objective function is different from that of the other. In solving such problems, with or without the presence of constraints, these problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solutions. Due to the multiplicity in solutions, these problems were proposed to be solved suitably using evolutionary algorithms which use a population approach in its search procedure. Starting with parameterized procedures in early nineties, the so-called evolutionary multi-objective optimization (EMO) algorithms is now an established field of research and application with many dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this chapter, we provide a brief introduction to its operating principles and outline the current research and application studies of EMO.