In this paper three evolutionary algorithms, i.e., genetic algorithm(GA), niche method for evolutionary programming(NM) and immune algorithm(IA), are applied to solve a so-called man scheduling problem where it is required to reduce the project duration time with a constraint on the total availability of certain key resource and also to level everyday resource demand. Evaluation of the proposed algorithms is extensively conducted using ten randomly created networks. Algorithm efficiency is evaluated by maximum fitness designated as F_max, mean fitness of ten best schedules, F_mean, and computation time. Model testing shows that GA and NM are comparable to each other based on the F_max and F_mean criteria although NM can be said to be more efficient than GA as far as computation time is concerned when network size gets big with nodes over 60 while IA almost consistently and substantially outperforms GA and NM with respect to fitness as well as computation time.