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Resource constrained project scheduling problem (RCPSP) is one of the classical problems in the area of discrete optimization. In this paper we propose an algorithm for solving RCPSP which relies on an adaptive insertion mutation operator that targets different regions of the search space. Neighbourhoods are exploited via forward-backward iterative local search. Furthermore, the algorithm makes use of an archive to ensure better utilization of the schedule budget. The performance of the approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLib with budgets of 1,000, 5,000 and 50,000 schedules. The study provides insights on the performance of the algorithm i.e. why the performance is good for particular instances and not as good for others.