This article presents a concise review of the scientific–technical computing system Maple and its application potentials in Operations Research. The primary emphasis is placed on non-linear optimization models that may be formulated using a broad range of components, including e.g. special functions, integrals, systems of differential equations, deterministic or stochastic simulation, external function calls, and other computational procedures. Such models may have a complicated structure, with possibly a non-convex feasible set and multiple, global and local, optima. We introduce the Global Optimization Toolbox to solve such models in Maple, and illustrate its usage by numerical examples.