Evolutionary Algorithms are search and optimisation methods based on the principles of natural evolution and genetics that attempt to approximate the optimal solution of a problem. Instead of only one, they evolve a population of potential solutions to the problem, using operators like mutation, crossover and selection. In this work, we present a new crossover operator, in the context of Multiobjective Evolutionary Algorithms, which makes use of the concept of Pareto optimality. After that it is compared to four common crossover operators. The results obtained are very promising.