This article presents a method for performing noise-constrained optimization of wind farms by changing the operational modes of the individual wind turbines. The optimization is performed by use of the TopFarm optimization framework and wind farm flow modelling in PyWake as well as two sound propagation models: the ISO 9613-2 model and the parabolic equation model, WindSTAR. The two sound propagation models introduce different levels of complexity to the optimization problem, with the WindSTAR model taking a broader range of parameters, like the acoustic ground impedance, the complex terrain elevation and the flow field from the noise source to the receptor, into account. Wind farm optimization using each of the two sound propagation models is therefore performed in different atmospheric conditions and for different source/receptor setups, and compared through this study in order to evaluate the advantage of using a more complex sound propagation model. The article focuses on wind farms in flat terrain including dwellings at which the noise constraints are applied. By this, the study presents the significant gain in using a higher fidelity sound propagation model like WindSTAR over the simple ISO 9613-2 model in noise-constrained optimization of wind farms. Thus, in certain presented flow cases a power gain of up to ∼53 % is obtained by using WindSTAR to estimate the noise levels.