Global electricity market deregulation makes compatible changes and new challenges in power system operation planning problem. Maintenance is required for the generating unit to reduce the risk of capacity outage and to improve availability of units and thereby extending equipment lifetime. Modified particle swarm optimization (MPSO) for the generator maintenance scheduling (MS) generates optimal, feasible solution and overcomes the limitation of the conventional methods such as extensive computational effort which increases exponentially as the size of the problem increases. The objective of this paper is to reduce the loss of load probability (LOLP) and maximize the profit of generating units using levelized risk method (LRM). Market participants submit the MS proposal based on market clearing price (MCP) and they request permission and receive approval for planned maintenance outages from the independent system operator (ISO) in competitive electricity markets. Mainly, we are concerned with a primary framework for ISO's maintenance coordination in order to determine LOLP values in the maintenance time intervals using LRM that uses LOLP convolution algorithm. The ISO will put forward its best endeavor to adjust individual generator maintenance schedules according to the estimated LOLP values. The proposed method is tested on five generating companies model of IEEE reliability test system (RTS) to demonstrate the effectiveness of the proposed method and the applicability of the elucidation scheme for large-scale MS coordination problems.