Response surface methodology (RSM) searches for the input combination that optimizes the simulation output. RSM treats the simulation model as a black box. Moreover, this paper assumes that simulation requires much computer time. In the first stages of its search, RSM locally fits first-order polynomials. Next, classic RSM uses steepest descent (SD); unfortunately, SD is scale dependent. Therefore, Part 1 of this paper derives scale independent 'adapted' SD (ASD) accounting for covariances between components of the local gradient. Monte Carlo experiments show that ASD indeed gives a better search direction than SD. Part 2 considers multiple outputs, optimizing a stochastic objective function under stochastic and deterministic constraints. This part uses interior point methods and binary search, to derive a scale independent search direction and several step sizes in that direction. Monte Carlo examples demonstrate that a neighborhood of the true optimum can indeed be reached, in a few simulation runs.