Due to the exponential increase in computational power and increasing awareness of problems associated with vadose zone parameter estimations based on laboratory and in situ measurements during the last decades, the process of automatic model calibration against laboratory or field data is being increasingly used. This is often referred to as inverse modeling and has as a major limitation the inability to identify a single optimal parameter set. A coupled HYDRUS1D-SCE (shuffled complex evolution) simulation global optimization technique was developed and its suitability for multiobjective inverse modeling evaluated. In particular, the trade-off between goodness of fit against leachate volume and soil moisture content in unirrigated and irrigated lysimeters was evaluated. After identification of the most sensitive model parameters using a Monte Carlo based sensitivity analysis, the technique was capable of finding effective Pareto optimal parameter sets that well simulated leachate volume and soil moisture content in both unirrigated and irrigated lysimeters. The parameter variation along the Pareto fronts was large and differences existed between soil hydraulic parameter distributions along the Pareto fronts of the irrigated and unirrigated treatments. The multiobjective optimization technique was then adopted for the verification of the suitability of the conceptual model of equal parameter sets for both treatments. The technique was able to objectively reject the hypothesis of equal parameter sets for both treatments. This is probably due to (i) physical parameter changes with time due to the effect that long-term irrigation has on soil structure, and (ii) differences in acting transport mechanisms between the unirrigated and irrigated lysimeters.