Functionalized magnetic nanoparticles are used in several biomedical applications, such as drug delivery, magnetic cell separation, and magnetic resonance imaging. Size and surface properties of iron oxide nanoparticles are the two important factors which could dramatically affect the nanoparticle efficiency as well as their stability. In this study, the chemometrics approach was applied to optimize the coating process of iron oxide nanoparticles. To optimize the size of nanoparticles, the effect of two experimental parameters on size was investigated by means of multivariate analysis. The factors considered were chitosan molecular weight and chitosan-to-tripolyphosphate concentration ratio. The experiments were performed according to face-centered cube central composite response surface design. A second-order regression model was obtained which characterized by both descriptive and predictive abilities. The method was optimized with respect to the percent of Z average diameter's increasing after coating as response. It can be concluded that experimental design provides a suitable means of optimizing and testing the robustness of iron oxide nanoparticle coating method.