This paper presents a new implementation of deterministic multiobjective (MO) optimization called Multiobjective Fractal Decomposition Algorithm (Mo-FDA). The original algorithm was designed for mono-objective large scale continuous optimization problems. It is based on a divide and conquer strategy and a geometric fractal decomposition of the search space using hyperspheres. Then, to deal with MO problems a scalarization approach is used. In this work, a new approach has been developed on a multi-node environment using containers. The performance of Mo-FDA was compared to state of the art algorithms from the literature on classical benchmark of multi-objective optimization