The Borg MOEA [8] is an optimization algorithm, designed to handle real-world problems of a multi objective and multimodal nature. In this report, we examine the effectiveness of Borg for solving optimization problems with only two objectives. To this end, we benchmark the performance of the algorithm on the bbob-biobj test suite via the COCO platform, comparing it to current state-of-the-art algorithms. The study uses standard values for all the parameters but one, as retrieved from http://borgmoea.org/. The only parameter that varies between different problem instances is the ε parameter, a crucial scale tuning parameter. To adapt this parameter, we devised and applied a heuristic. We find that the algorithm performs respectably, although it does not surpass the current state-of-the-art algorithms for any of the problem instances examined, and particularly loses performance on problems with a high-dimensional search space. Additionally, we observed that our heuristic for tuning the ε-parameter results in significant performance improvements compared to using a fixed value for ε.