Polypeptides are becoming an important component of the antibiotic therapeutics. The production demand of therapeutic polypeptides is increasing and there is a significant interest in developing more efficient production processes. In pharmaceutical industries, polypeptides are produced as a crude mixture. Reverse phase high performance liquid chromatography (RP HPLC) is used as a typical separation technique to purify the target polypeptide from other impurities. Currently organic modifier gradients are used to elute product peptides separately from impurities. In this work, we add a second, simultaneous counter-ion gradient, in the hope of increasing separation performance and call it double gradient reverse phase chromatography. A general procedure of the model-based optimization of a polypeptide crude mixture purification process was followed to evaluate the effects of the double gradients on industrial chromatographic process. The target polypeptide elution profile was modeled with a bi-Langmuir adsorption equilibrium isotherm. The isotherm parameters of the target polypeptide were estimated by the inverse method. The model parameters of the impurities were regressed from experimental data. The variations of the isotherm parameters with the modifier concentration and counter-ion concentration were taken into account of the adsorption model. After model calibration and validation by comparison with suitable experimental data, Pareto optimization of the process were carried out to analyze the differences between single gradient chromatography and double gradient chromatography. It was observed that the additional linear gradient of counter-ion concentration did not improve the separation process. Conclusively we were able to demonstrate the concept of double gradient reverse phase chromatography within limited time and possible least experimental efforts.