We dwell in largely non-technical terms on the essential differences between single-objective optimization and multiple-objective optimization. We argue in particular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which make many ideal solutions unreachable to the optimization method. We promote the use of multi-objective optimization methods, particularly those arising from the evolutionary computation community. We point out that the state of the art in the field of evolutionary multi-objective optimization is such that fast and effective techniques are now available which are capable of finding a well-distributed set of diverse trade-off solutions, with little or no more computational effort than sophisticated single-objective optimizers would have taken to find a single one. The resulting diversity of ideas available through a multi-objective approach leads both to the problem-solver being furnished with a better understanding of the space of possible solutions, and consequently to a better final solution to the problem at hand. We end by very briefly charting the history of the field and hinting at the range of published applications and ongoing research issues.