Many real-world classification systems must comply with a series of inherent restrictions to the problem at hand such as response times, power consumptions or computational costs. This poses a fundamental limitation to traditional performance-driven classifiers and learning algorithms by restraining their applicability in cost-sensitive scenarios. Because of this, fuzzy systems are leveraged to learn cost-conscious multi-stage classifiers through multiobjective optimization to find a set of optimal tradeoffs between accuracy and any related cost. This approach allows find a suitable balance between all objectives regardless of the scenario. Experimental evaluations were done for Sound Environment Classification in modern battery-powered hearing aids by jointly optimising classification accuracy and computational costs.