Real world applications are exposed to various uncertainties. In deterministic design philosophy safety factors are usually applied to ensure structural integrity under worst-case conditions. This worst-case design philosophy often applied in industrial practice is assumed to introduce a high degree of conservatism, leading to a non-optimal and oversized designs. Hence, to remedy this shortcoming, several approaches have been proposed considering uncertainties during optimization. Usually, uncertainty including optimization approaches are validated by the Monte Carlo method, which is an easy to simple, general applicable and broadly accepted technique. Hence, it is applied in this study to propagate and quantify uncertainties in a robust topology optimization framework. Four different independent types of random parameters (i.e. load, geometry, material stiffness and strength) are considered simultaneously while constraining the 1%-quantile of a load capability factor. In most studies, the designs obtained from robust or reliability-based optimization are compared to deterministic optimized designs without safety factors. In this study, aiming at a fair comparison, a worst-case topology optimization is formulated, for which safety factors are defined based on an assumed 1% failure probability. Results show, that the robust formulation yield designs which meet desired constraints, while the worst-case formulation overshoots slightly.