We developed an algorithm to identify and determine periods of variable sources. With its robustness and high speed, it is expected to become an useful tool for surveys with large volume of data. This new scheme consists of an initial coarse. process of finding several candidate periods followed by a secondary process of much finer period search. With this multi-step approach, best candidates among statistically possible periods are produced without human supervision and also without any prior assumption on the nature of the variable star in question. We tested our algorithm with 381 stars taken from the ASAS survey and the result is encouraging. In about $76\%$ cases, our results are nearly identical as their published periods. Our algorithm failed to provide convincing periods for only about $10\%$ cases. For the remaining $14\%$, our results significantly differ from their periods. We show that, in many of these cases, our periods are superior and much closer to the true periods. However, the existence of failures, and also periods sometimes worse than manually controlled results, indicates that this algorithm needs further improvement. Nevertheless, the present experiment shows that this is a positive step toward a fully automated period analysis for future variability surveys.