Multi-objective optimization under uncertainty is an important line of research that has seen a great interest in recent years. Our aim is to deal with multi-objective problems with fuzzy data expressed by means of triangular fuzzy numbers. As a consequence, the objective functions to be optimized in this case will be disrupted by the used fuzzy form. To this end, we first propose a new Pareto approach for ranking the generated triangular-valued functions. Then, based on the proposed approach, we introduce a fuzzy extension of two well-known multi-objective evolutionary algorithms: SPEA2 and NSGAII in order to enable them handling such problems. An application on a multi-objective vehicle routing problem with uncertain demands is finally performed and validated through an experimental study.