In this study, response surface methodology (RSM) was discussed as an efficient method for the optimization of Pb(II) adsorption onto Fe₃O₄ nanoparticles and chitosan-coated Fe₃O₄ particles. To retain the non-linearity of isotherm/kinetic models and inherent characteristics of mechanism involved in adsorption process, the model parameters in these models are estimated using differential evolution (DE) based hybrid optimization. The lack of fit for first-order response-surface model and two-way interactions model were insignificant, but a higher lack-of-fit was observed in the second-order model and reduced second-order model. The results of these computations (F-value: 29.3 on 6 and 37 DF; p-value: 1.226e⁻¹²; R²: 0.8261, and LOF: 0.99) showed that the RSO model fit well with Pb (II) removal by Ch-Fe. Based on the RSM model, the optimal conditions were found to be 10.95, 5.5 mg/L, 66.59 min, and 0.1 g/L for pH, Ch-Fe dosage, initial Pb (II) concentration and residence time, respectively, resulting in maximum (93.6%) removal efficiency. Based on the findings, the model predictions from the DE based optimized parameters provided optimal parameter sets which better represent the adsorption rate models and infer the inherent mechanisms of the adsorption process.