This study used field lysimeter leachate and pesticide concentration data within an inverse modeling framework to estimate pesticide degradation and sorption parameters. Experimental data comprising four pesticide applications during 3 yr were used to compare a local parameter estimation algorithm (Levenberg–Marquardt, LM) with a global algorithm (Shuffled Complex Evolution Metropolis, SCEM). Good model fits (only marginally better model fits using SCEM) with respect to both the observed leachate volumes and corresponding pesticide concentrations were obtained using both algorithms. Parameter optima found with LM and SCEM were very similar, thus suggesting that LM correctly located the global optimum for our experimental data. Equally as important as the optimal parameter values, however, are the estimated parameter uncertainties. This study revealed that LM (using a Jacobian-based approach) provided too large parameter uncertainties. A logarithmic transformation of the parameter tended to decrease the uncertainty in most cases. The overestimation of parameter uncertainty by LM suggests that model sensitivity close to the optimal parameter set was relatively small and underestimated the sensitivity to large parameter changes. A multiobjective Pareto analysis was subsequently compared with a sequential single-objective approach to reveal the capability of the multiobjective approach to verify model structure and model concept. Our results indicate that a multiobjective SCEM approach is recommended when the objective is to estimate pesticide degradation and sorption parameters and their uncertainty.