Optimization of the operating parameters for membrane separation of proteins is usually performed in industry by varying one factor at a time. While this procedure cannot ensure that an optimum is found, it is generally too time-consuming and costly to determine optimal operating conditions by simultaneously varying all factors. This paper presents a procedure that can be generically used by, for example, the biotechnology industry, for determining close to optimal operating conditions for cases where relatively little data are available. The procedure fits a polynomial regression model to the operating parameters and performs a multidimensional optimization of the empirical model with regard to some important operating parameter, for example, a combination of selectivity and purity. The technique is illustrated with a membrane ultrafiltration process for the separation of hen egg lysozyme from ribonuclease A and in a second case study for separating hemoglobin from bovine serum albumin.