In multicriteria optimization determination of the Pareto-optimal front is of utmost importance for decision making. Simultaneous parallel search for multiple members of an evolutionary algorithm can lead to effective optimization. In a previous approach (Esquivel et al., 1999) extending the ideas of a former work of (Lis and Eiben, 1997), we proposed the multi-sexual-parents-crossovers genetic algorithm (MSPC-GA), a method which by allowing multiple parents per sex and multiple crossovers per mating action attempted to balance the explorative and exploitative efforts which are present in any evolutionary algorithm. The performance of the method produced an evenly distributed and larger set of efficient points. Following this concept the present proposal incorporates a hybridisation of global and local search to the multiplicity approach. Now the evolutionary approach combined with simulated annealing and neighbourhood search produced better results.