To effectively respond to the volatile environment, the project schedule should be adaptable in a timely manner when uncertain events occur during the project process. The paper introduces a two-phase time-based robust shift genetic algorithm, designed to efficiently generate schedules for multi-resource flexible projects in the presence of uncertainties. The first phase involves developing a critical path analysis to select not only the appropriate mode but also determine the best resource type for each activity, considering renewable and non-renewable resources. The primary objective of this phase is to minimize the total makespan (Cmax) of the project. In the second phase, the project schedule is updated in response to uncertain events, with the stochastic project’s completion time constraint, minimizing the overall cost. A time-based robust measure is calculated for each task, utilizing historical data. This measure serves as the foundation for a novel time-based robust shift operation, incorporated with the genetic algorithm’s crossover and mutation operations to produce flexible project schedule solutions. To validate the performance of the proposed approach, numerical experiments were conducted using datasets from the standard “Project Scheduling Problems Library” (PSPLIB), yielding promising results.