Genetic algorithms are used in scheduling leading to efficient heuristic methods for large sized problems. The efficiency of a GA based heuristic is closely related to the quality of the used GA scheme and the GA operators. The main focus of this paper is the use of new data oriented operators. The data in question could be, for instance, the maximum processing times among the jobs, the due dates of the tasks and so forth. The crossover or mutation is then done using this data. In this paper we consider the permutation flowshop problem. Minimization of the total weighted tardiness is taken as the objective function. We perform a series of experiments for m=10 machines and n=20 jobs. The machines are not identical and there may be bottleneck machines in the system. We compare our results with the results obtained from the classical GA operators. Our preliminary results prove to be encouraging in terms of the convergence speed and the total weighted tardiness.