Optimizing Multiple Models...any suggestions?

There are multiple marketing models in place to predict individual-level
probabilities of whether or not someone would respond to a solicitation,
whether or not they would become a customer, and if they did become a
customer, how much money the company is likely to make.  Each individual
receives a score from each model, and the final goal is to rank all
individuals based on a final score.  As long as multiple models exist, I'm
guessing the method of deriving a final score from multiple model scores is
an optimization algorithm.

I have no experience with optimization in R, and was wondering if anyone
could recommend a package or function, or any other method, for this
particular type of problem.  Thanks.