The present work assesses different Monte Carlo methods in radiative heat transfer problems, in terms of accuracy and computational cost. Achieving a high scalability on numerous CPUs with the conventional forward Monte Carlo method is not straightforward. The Emission-based Reciprocity Monte Carlo Method (ERM) allows to treat each mesh point independently from the others with a local monitoring of the statistical error, becoming a perfect candidate for high-scalability. ERM is however penalized by a slow statistical convergence in cold absorbing regions. This limitation has been overcome by an Optimized ERM (OERM) using a frequency distribution function based on the emission distribution at the maximum temperature of the system. Another approach to enhance the convergence is the use of low-discrepancy sampling. The obtained Quasi-Monte Carlo method is combined with OERM. The efficiency of the considered Monte-Carlo methods are compared.