A method for performing robust optimal design that combines the efficiency of experimental designs and the accuracy of nonlinear programming (NLP) has been developed, called Search-and-Zoom. Two case studies from the RF and communications industry, a high-frequency micro-strip band-pass filter (BPF) and a rectangular, directional patch antenna, were used to show that sensitivity optimization could be effectively performed in this industry and to compare the computational efficiency of traditional NLP methods (using fmincon solver in MATLAB R2013a) and they hybrid method Search-and-Zoom. The sensitivity of the BPF's S11 response was reduced from 0.06666 at the (non-robust) nominal optimum to 0.01862 at the sensitivity optimum. Feasibility in the design was improved by reducing the likelihood of violating constraints from 20% to nearly 0%, assuming RSS (i.e., normally-distributed) input tolerances and from 40% to nearly 0%, assuming WC (i.e., uniformly-distributed) input tolerances. The sensitivity of the patch antenna's S11 function was also improved from 0.02068 at the nominal optimum to 0.0116 at the sensitivity optimum. Feasibility at the sensitivity optimum was estimated to be 100%, and thus did not need to be improved. In both cases, the computation effort to reach the sensitivity optima, as well as the sensitivity optima with RSS and WC feasibility robustness, was reduced by more than 80% (average) by using Search-and-Zoom, compared to the NLP solver.