A resort is consisted of amenities and supporting resources (e.g., infrastructure). Some popular amenities alone may attract tourists while others may only attract tourists when they are simultaneously accessible. The inter-dependences among the amenities and supporting resources make the development problem difficult for human experts to optimize. This research presents a new decision-support model, called Resort Investment Planner (RIP), by integrating Monte Carlo simulation and polyploidy genetic algorithms (GAs) to optimize the development levels of resort amenities in each project phase. An experiment with a real project case in Taiwan showed that the development plan produced with RIP took less time (62% faster) with higher net present value (30% higher) than the averages of human experts.