This publication presents a numerical approach for analyzing tires based on multiobjective optimization, with particular consideration of uncertainties. Within the optimization, which uses evolutionary algorithms, the evaluation of a three-dimensional, finite element tire model at steady-state rolling is performed. To obtain a reliable and high-quality design, data uncertainty caused, e.g., by variation in production conditions of the tire components, as well as incomplete information concerning loading, have to be considered. Among several design goals, this study looked at durability as an example. An improvement is achieved by the consideration of two objective functions: one focusing on reducing wear, and the other on providing resistance to fatigue. In addition, the proposed optimization measures robustness implicitly. A tire model is regarded as robust when large variations of the uncertain influencing factors mentioned, e.g., loading or material properties, lead to only minor variations in uncertain structural responses, e.g., strains, stresses, or contact pressures. To improve the numerical efficiency of the proposed design approach, a response-surface approximation, based on artificial neural networks, is applied.