A multistart optimization algorithm for surface complexation equilibrium parameters (MUSE) was applied to a large and diverse data set for chromate adsorption on iron (oxy)hydroxides (ferrihydrite and goethite). Within the Basic Stern and the charge-distribution multisite complexation (CD-MUSIC) framework, chromate binding constants and the Stern Layer capacitance were optimized simultaneously to develop a consistent parameter set for surface complexation models. This analysis resulted in three main conclusions regarding the model parameters: (a) There is no single set of parameter values that describes such diverse data sets when modeled independently. (b) Parameter differences among the data sets are mainly due to different amounts of total sites, i.e., surface area and surface coverages, rather than structural differences between the iron (oxy)hydroxides. (c) Unified equilibrium constants can be extracted if total site dependencies are taken into account. The implementation of the MUSE algorithm automated the process of optimizing the parameters in an objective and consistent manner and facilitated the extraction of predictive relationships for unified equilibrium constants. The extracted unified parameters can be implemented in reactive transport modeling in the field by either adopting the appropriate values for each surface coverage or by estimating error bounds for different conditions. The evaluation of a forward model with unified parameters successfully predicted chromate adsorption for a range of capacitance values.