In this paper, an automated method is proposed for optimising the real-valued parameters of a hybrid Machine Translation (MT) system that employs pattern recognition techniques together with extensive monolingual corpora in the target language from which statistical information is extracted. The absence of a parallel corpus prohibits the use of the training techniques traditionally employed in state-of-the-art Statistical Machine Translation systems.The proposed approach for fine-tuning the system parameters towards the generation of high-quality translations is based on a Genetic Algorithm and the multi-objective evolutionary algorithm SPEA2. In order to evaluate the translation quality, established MT automatic evaluation criteria are employed, such as BLEU and METEOR. Furthermore, various ways of combining these criteria are explored, in order to exploit each one’s characteristics and evaluate the produced translations. The experimental results indicate the effectiveness of this approach, since the translation quality of the evaluation sentence sets used is substantially improved in all studied configurations, when compared to the output of the same system operating with manually-defined parameters. Out of all configurations, the multi-objective evolutionary algorithms, combining several MT evaluation metrics, are found to produce the highest quality translations.