Over the past decades, Evolutionary Computation (EC) has surfaced as a popular paradigm in the domain of computational intelligence for global optimiza- tion of complex multimodal functions. The distinctive feature of an Evolutionary Algorithm (EA) is the emergence of powerful implicit parallelism as an offshoot of the simple rules of population-based search. However, despite the known advantages of implicit parallelism, it is interesting to note that EAs have almost exclusively been developed to solve only a single optimization problem at a time; seldom has any effort been made to multitask, i.e., to tackle multiple self-contained optimization problems concurrently using the same population of evolving individuals. To this end, inspired by the remarkable ability of the human brain to perform multiple tasks with apparent simultaneity, we present evolutionary multitasking as an intriguing direction for EC research. In particular, the paradigm opens doors to the possibility of autonomously exploiting the underlying complementarities between separate (but possibly simi- lar) optimization exercises through the process of implicit genetic transfer , thereby enhancing productivity in decision making processes via accelerated convergence characteristics. Along with the design of an appropriately unified solution represen- tation scheme, we present the outline of a recently proposed algorithmic framework for effective multitasking. Thereafter, the efficacy of the approach is substantiated through a series of practical examples in continuous and discrete optimization that highlight the real-world utility of the paradigm.