In this work we present a methodology for optimal management of brownfields that is illustrated on a real field. The approach does not depend on the particular reservoir flow simulator used, although streamline-derived information is leveraged to accelerate the optimization. The method allows one to include (non-linear) constraints (e.g. a recovery factor larger than a given baseline value), which are very often challenging to address with optimization tools. We rely on derivative-free optimization coupled with the filter method for non-linear constraints, although the methodology can also be combined with approaches that utilize exact/approximate gradients. Performance in terms of wall-clock time can be improved further if distributed-computing resources are available (the method is amenable to parallel implementation). The methodology is showcased using a real field in west Siberia where net present value (NPV) is maximized subject to a constraint for the recovery factor. The optimization variables represent a discrete time series for well bottom-hole pressure over a fraction of the production time frame. An increase in NPV of 7.9% is obtained with respect to an existing baseline. The optimization methods studied include local optimization algorithms (e.g. generalized pattern search) and global search procedures (e.g. particle swarm optimization). The controls for one injection well in the real field were actually modified according to the solution determined in this work. The results obtained suggest improvement for most economic scenarios.