Semi-infinite programming has received little attention in the non-convex case. Indeed, the problem is known to be NP-hard, and existing methods focus on the convex-concave particular case. Interval analysis enables to solve globally the general case of Semi-infinite programming. That is, the quantified constraints can be dealt with in a guaranteed way, and the convergence to the global minimum is ensured. However, the complexity of algorithm based on Interval Analysis, such as Branch and Bound algorithm, is exponential with respect to the number of variables. In order to shorten the convergence time of such algorithm, heuristics can be used. Those heuristics are based both on the monotonicity of inclusion function, as defined by interval arithmetics, and the reformulation of the quantified constraint as a maximization problem.