This paper presents and compares different algorithms on large scale p-median problems, up to 2000 candidate nodes. Our main focus is instances where the demand is asymmetric distributed. We use as real data the Swedish road network including distances and as demand points the location of Swedish citizens as our experimental context. Tested methods simulated annealing, volume algorithm and Cplex. Our new hybrid genetic approach outperforms other existing approaches on large instances.