Surfactant-enhanced aquifer remediation (SEAR) is widely considered a promising technique to remediate dense nonaqueous phase liquid (DNAPL) contaminations in-situ. The costs of a SEAR remediation are important and depend mostly on the setup of the remediation. Costs can be associated with the installation of injection and extraction wells, the required time of the remediation (and thus labor costs, lease of installations, and energy), the extracted water volume (the purification of the extracted water), and the injected surfactant amount. A cost-effective design of the remediation setup allows an optimal use of resources. In this work, a SEAR remediation was simulated for a hypothetical typical DNAPL contamination. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL mass (ii) with a minimal total cost. A relatively sharp Pareto front was found, showing a considerable tradeoff between DNAPL removal and total remediation costs. These Pareto curves can help decision makers select an optimal remediation strategy in terms of cost and remediation efficiency depending on external constraints such as the available budget and obligatory remediation goals.