Data sharing is important in the medical domain. Sharing data allows large-scale analysis with many data sources to provide more accurate results (especially in the case of rare diseases with small local datasets). Cloud federations can leverage sharing medical data stored in different cloud platforms, such as Amazon, Microsoft, Google Cloud, etc. They also enable access to distributed data of patients. The pay-as-yougo model in cloud federations raises important issues of Multi-Objective Optimization Problems (MOOP) related to users’ preferences, such as response time, money, quality, etc. However, optimizing a query in a cloud federation is complex with increasing the variety, especially due to a wide range of communications and pricing models. The variety of virtual machines configuration also leverages the high complexity in generating the space of candidate solutions. Indeed, in such a context, it is difficult to provide accurate estimations and optimal solutions to make relevant decisions. The first challenge is how to estimate accurate parameter values for MOOPs without precise knowledge of the execution environment in a cloud federation consisting of different sites. To address the accurate estimation of parameter values problem, we present the Dynamic Regression Algorithm (DREAM), which can provide accurate estimations in a cloud federation with limited historical data. DREAM focuses on reducing the size of historical data while maintaining the estimation accuracy. The second challenge is how to find an approximate optimal solution in MOOPs using an efficient Multi-Objective Optimization algorithm. To address the problem of finding an approximate optimal solution, we present Non-dominated Sorting Genetic Algorithms based on Grid partitioning (NSGA-G) for MOOPs. The proposed algorithm is integrated into the Intelligent Resource Scheduler, a solution for heterogeneous databases, to solve MOQP in cloud federations. We validate our algorithms with experiments on a decision support benchmark (TPC-H benchmark).