A New Genetic Algorithm to Solve Effectively Highly Multi-Objective Problems: POGA. This paper presents a new MOGA, the Preference Ordering Genetic algorithm: POGA. It is largely inspired by NSGA-II [10], a most acknowledged MOGA that has proved successful in a number of applications, but in contrast it embodies Preference Ordering in its ranking scheme to drive the search for better solutions. The paper is organized as follows: the first section provides the theoretical background and a detailed description of the algorithm is given in the second one. The third and fourth sections compare the results of the application of the two algorithms to the optimization of a test function and to the automatic calibration of an urban drainage model respectively. A particular emphasis is given to the issue of scalability and to support that POGA is particularly efficient to tackle highly dimensional MOPs. Finally, the results are discussed and unexplored research issues suggested.