This work sets out the creation, verification and demonstration of an automated end-to-end tool for the multi-point fluid-dynamic optimization of turbomachinery. Taking into account multiple operating conditions during optimization aims to benefit the overall performance of turbomachines which are characterized by off-design operation and to produce more robust designs with respect to deviations in operating conditions. The feasibility and effectiveness of the tool is demonstrated by a two-point fluid-dynamic optimization of a two-dimensional Aachen turbine stator cascade, performed on the High-Performance Computing cluster of the TU Delft. By utilizing the stochastic NSGA2 optimization algorithm, the numerically noisy problem is successfully optimized. The entropy generation of the blade is reduced at both nominal and off-design operating condition by an average of 7.57%, while satisfying a constraint on the flow turning when passing through the stator cascade. Additionally, a numerical noise analysis shows that disregarding the most noisy design variables increases the probability of obtaining an improved design.