This research aims to solve goal-setting and coping strategies of resource utilization and energy-saving & consumption-reducing in Iron Mine mining and milling management. The mining & milling system is divided into the subsystems of production, resource utilization, and energy consumption, a multi-objective constrained optimization problem (COP) model is established to obtain the optimal production grades (cut-off grade and milling grade) and the goals of resource comprehensive utilization and energy consumption; then system dynamics (SD ) model is established to reveal the mechanism of resource loss and energy consumption; the key points of resource loss and energy consumption could be found out by sensitivity analysis; basing on these key points, the paths are designed for resource conservation and energy saving; the optimal path would be seek out from the alternative paths by SD simulation, to get the control strategies. The above contents formulate a chain of goal- key point- path - strategy. The particle swarm optimization (PSO), artificial neural network (ANN) and differential evolution (DE) are integrated to be hybrid intelligent algorithm (IA), to solve the COP model, and to optimize the parameters and variables of SD model. This study could enrich the theory and methodology of sustainable development of mineral resources, and provide specific and feasible method and policy of resource conservation and energy saving for China's Iron Mine enterprises.