It is a significant challenge to reliably identify fate and transport parameters for solutes that simultaneously undergo multiple and complex processes. Evolutionary optimization techniques allow process parameters to be identified for complicated problems. One evolutionary optimization technique, the stochastic ranking evolutionary strategy (SRES), was used in this study to find the global or near-global optimal parameters for two solute breakthrough curve experiments. The uncertainties of the estimated parameters were assessed using a nonparametric bootstrap resample method. The two miscible-displacement cases considered were (i) a simulated experiment of a solute undergoing transformation and (ii) an actual experiment of trichloroethylene undergoing simultaneous nonequilibrium transport and transformation. In both cases, multiple breakthrough curves were produced from a parent solute and its metabolites, and the SRES was used with an appropriate convective–dispersive model to inversely identify the model parameters. The SRES inverse method provided an excellent model fit to the experimental data, and the model parameter estimates had minimal uncertainty even when 5% experimental error was introduced.