Novel systems allocating coalitions of humans and unmanned heterogeneous vehicles will act as force multipliers for future real-world missions. Conventional coalition formation architectures seek to compute efficient robot coalitions by leveraging either a single greedy, approximation, or market-based algorithm, which renders such architectures inapplicable to a variety of real-world mission scenarios. A novel, intelligent multi-criteria decision making framework is presented that reasons over a library of coalition formation algorithms for selecting the most appropriate subset of algorithm(s) to apply to a wide spectrum of complex missions. The framework is based on influence diagrams in order to handle uncertainties in dynamic real-world environments. An existing taxonomy comprised of multiple mission and domain dependent features is leveraged to classify the coalition formation algorithms. Dimensionality reduction is achieved via principal component analysis, which extracts the most significant taxonomy features crucial for decision making. A link analysis technique provides the mission specific utility values of each feature-value pair and algorithm in the library. Experimental results demonstrate that the presented framework accurately selects the most appropriate subset of coalition formation algorithm(s) based on multiple mission criteria, when applied to a number of simulated real-world mission scenarios.