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The objective of this paper is to optimize maintenance scheduling of generating units of a captive thermal plant using intelligent optimization techniques such as genetic algorithm (GA) and simulated annealing (SA). A multi-class maintenance scheduling has been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. Such multi-class maintenance scheduling in terms of mathematical formulations necessitates a planning horizon of five years compared to the conventional one to two year horizon. As the utilities catered by captive power plants are very sensitive to power failure, the reliability of power supply has been evaluated by loss of load probability (LOLP) index. The significant contribution of this paper is to evaluate confidence intervals of LOLP instead of conventional point estimate of LOLP. A point-estimate of LOLP uses expected values and provides no information about the range of values over which LOLP may vary. Some variations from optimum schedules are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are considered in terms of near-optimum schedules obtained from SA during the final stages of convergence. LOLP evaluated from such sub-optimum schedules forms the basis to treat LOLP as a random variable for subsequent evaluation of confidence intervals. Case studies pertaining to a captive power plant for an aluminium smelter corroborate that the schedules obtained from two optimization techniques yield the same optimum schedule and thus the efficacy of the proposed algorithm is validated. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power. [ABSTRACT FROM AUTHOR]