Task matching and scheduling by genetic-algorithm-based approaches have been attractive problems. Standard genetic operators are not always suitable for task matching and scheduling based on permutation representation. Genetic operators are important for genetic algorithms. Three genetic operators are proposed: improved crossover(IMCX), internal crossover(INCX), and migration which transfers a task from a processor to another within a schedule as a kind of mutation. Simulation results and analysis show that these genetic operators are effective for task matching and scheduling.