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The Constraint Satisfaction Problem (CSP) framework allows users to define problems in a declarative way, quite independently from the solv-ing process. However, when the problem is over-constrained, the answer “no solution ” is generally unsatisfactory. A Max-CSP Pm = 〈V,D, C 〉 is a triple defining a constraint problem whose solutions maximize the number of satisfied constraints. In this paper, we focus on numerical CSPs, which are defined on real variables represented as floating point intervals and which constraints are numerical relations defined in intension. Solving such a problem (i.e., exactly characterizing its solution set) is generally undecidable and thus consists in providing approximations. We propose a Branch and Bound algorithm that provides under and over approxima-tions of a solution set that maximize the maximum number mP of satisfied constraints. The technique is applied on three numeric applications and provides promising results.