Increasing popular big data applications bring about invaluable information, but along with challenges to industrial community and academia. Cloud computing with unlimited resources seems to be the way out. However, this panacea cannot play its role if we do not arrange fine allocation for cloud infrastructure resources. In this paper, we present a multi-objective optimization algorithm to trade off the performance, availability, and cost of Big Data application running on Cloud. After analyzing and modeling the interlaced relations among these objectives, we design and implement our approach on experimental environment. Finally, three sets of experiments show that our approach can run about 20 percent faster than traditional optimization approaches, and can achieve about 15 percent higher performance than other heuristic algorithms, while saving 4 to 20 percent cost.