Parallel master-slave evolutionary algorithms easily lead to linear speed-ups in the case of a small number of nodes \ldots and homogeneous computational costs of the evaluations. However, modern computer now routinely have several hundreds of nodes -- and in many real-world applications in which fitness computation involves heavy numerical simulations, the computational costs of these simulations can greatly vary from one individual to the next. A simple answer to the latter problem is to use asynchronous steady-state reproduction schemes. But the resulting algorithms then differ from the original sequential version, with two consequences: First, the linear speed-up does not hold any more; Second, the convergence might be hindered by the heterogeneity of the evaluation costs. The multi-objective optimization of a diesel engine is first presented, a real-world case study where evaluations require several hours of CPU, and are very heterogeneous in terms of CPU cost. Both the speed-up of asynchronous parallel master/slave algorithms in case of large number of nodes, and their convergence toward the Pareto Front in case of heterogeneous computation times, are then experimentally analyzed on artificial test functions. An alternative selection scheme involving the computational cost of the fitness evaluation is then proposed, that counteracts the effects of heterogeneity on convergence toward the Pareto Front.