Load demand levels in power networks are continuing to increase and as a result bus voltages are requiring more support from generators elsewhere in the network in order to maintain the voltage within specified limits. If the network is unable to support the increasing demand, bus voltage will begin to degrade until the point of voltage collapse which can lead to catastrophic network failure. Previous studies have shown that evolutionary computing techniques are effective methodologies for locating voltage collapse points. Ant Colony Optimization techniques allow for the optimization of many independent parameters simultaneously, the loading parameter for each bus is considered in this work. In this study Ant Colony Optimization is applied to detect voltage collapse conditions in power networks, to obtain faster computing time with the future goal of providing online detection and prediction for use in smart grids. Results and conclusions drawn from this study are also presented.