By now evolutionary multi-objective optimization (EMO) is an established and a growing field of research and application with numerous texts and edited books, commercial software, 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 discuss the principles of EMO through an illustration of one specific algorithm and an application to an interesting real-world bi-objective optimization problem. Thereafter, we provide a list of recent research and application developments of EMO to paint a picture of some salient advancements in EMO research. Some of these descriptions include hybrid EMO algorithms with mathematical optimization and multiple criterion decision-making procedures, handling of a large number of objectives, handling of uncertainties in decision variables and parameters, solution of different problem-solving tasks better by converting them into multi-objective problems, runtime analysis of EMO algorithms, and others. The development and application of EMO to multi-objective optimization problems and their continued extensions to solve other related problems has elevated the EMO research to a level which may now undoubtedly be termed as an active field of research with a wide range of theoretical and practical research and application opportunities.