In a grayscale masked stereolithography apparatus (gMSLA), a liquid polymer resin is selectively exposed to UV light in a layer-by-layer fashion to create three-dimensional structures. The light exposure of each point can be adjusted accurately by varying the local grayscale value displayed on a projection LCD mask situated below the resin tank. This determines where the material is cured or remains liquid within each layer. The stereolithography process stands out from other printing processes due to its high resolution. Applications include, for example, the fabrication of microfluidic devices and dental models. However, it is observed that the properties and geometry are highly dependent on the process parameters. Thus, the dimensional conformity of the 3D printed objects is still a challenge. Due to light propagation effects, overcuring is observed such that there is a mismatch between the original CAD geometries and the print results. A simulation of the printing process enables an accurate prediction of the geometry and properties of the printed object. The process model provides the basis for subsequent optimization. The input parameters, particularly the grayscale input, are optimized such that errors due to the printing process can be compensated. Thereby, the accuracy of the printed parts can be improved. However, the optimization of larger parts is still a challenge due to the large number of design variables. It is investigated how the optimization procedure can be scaled up to simulate and optimize prints with dimensions similar to the available volume of the printer. Different ways to formulate the optimization problem are investigated to identify the most suitable version. The geometry can be represented on a smaller scale compared to the LCD resolution, harnessing the full potential of printing with a grayscale mask.