This　paper　describes　a　non-generational　GA　for　multi-objective　optimization　problems　(MOP)　based　on　a　crossover　operator　called　DC　(Dislocation　Crossover).　In　it　the　replacement　policy　is　such　that　an　offspring　replaces　the　worst　one　in　the　current　population　only　if　it　is　better　than　it.　And　in　this　algorithm　every　element　in　the　population　a　domination　count　is　defined　together　with　a　neighborhood　density　measure　based　on　a　sharing　function.　Those　two　parameters　are　then　non-linear　combined　in　order　to　define　the　individual＇s　fitness.　Computer　simulation　is　performed,　the　results　suggesting　that　the　non-generational　scheme,　combined　with　the　DC　crossover,　can　lead　to　a　uniform　group　of　non-dominated　solutions.