As mixed integer programming (MIP) problems become easier to solve in practice, they are used in a growing number of applications where producing a unique optimal solution is often not enough to answer the underlying business problem. Examples include problems where some optimization criteria or some constraints are difficult to model, or where multiple solutions are wanted for quick solution repair in case of model changes. In this talk, we address the problem of effectively generating multiple solutions for the same model, concentrating on optimal and near-optimal solutions. We present a modification of the branch-and-cut algorithm that allows it to produce multiple solutions. We then show that it significantly outperforms previously known algorithms in terms of the speed to generate multiple solutions, while providing an acceptable level of diversity in the solutions produced.