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In this paper the author proves that efficiency of multi-objective algorithms can be compared to single-objective algorithms for scheduling jobs in grid environment. Algorithms are compared via efficiency of reaching best solutions given by objective function. There are two criteria (computation speed and computation cost) presented in objective function including users weights on those criteria. Single-objective algorithms are represented by genetic algorithm and simulated annealing. Class of multi-objective algorithms is represented by improved strong Pareto evolutionary algorithm (SPEA2) and archived multi-objective simulated annealing (AMOSA). Algorithms are compared with best available results (by setting the best input parameters found) in ten, twenty, forty, sixty, eighty and one hundred second runs for one hundred experiments each.