Organic Rankine Cycle (ORC) processes are used to convert low-temperature waste heat into electricity. Often, these sources and sinks are sensible heat flows. In such a case, it may be appropriate to adapt the cycle process to these external temperature characteristics. This can be realized with zeotropic mixtures as a working fluid and evaluated by an exergy calculation and the exergetic efficiency. In addition to the usual degrees of freedom for the optimization of conventional ORC processes, the composition of the mixture is therein also a varying parameter. This depends on the predefined parameters of the source and sink, such as the mass flows or outlet temperatures of the system, as well as the outlet power or auxiliary power consumption of the components. Particularly in small-scale systems, the economic aspects play an overriding role so that constraints such as the minimal power density or maximum volume flow rates of volumetric expanders should be included in the optimization process. For this purpose, a two-stage optimization tool is presented which determines the optimal composition for up to quaternary mixtures and takes constraints of the source, the sink, and components used into account. In a time-saving analytical pre-calculation, the process can already be optimized and parametric studies or initial value variations can easily be carried out. The results serve as input parameters for a numerical post-calculation, which models the cycle in detail and performs further optimization. The functionality of this tool is illustrated by means of an ORC system with an air-cooled condenser at a sensible waste heat source temperature of 90 to 120 °C and initial results and observations are presented. Two mixtures with the respective preselected components R-1234ze(E), R-1224yd(Z), R-1233zd(E) and R-1336mzz(Z) as well as R-290, R-600a, R-600 and R-601 are used for this purpose. It was proven that higher exergetic efficiencies and net power can be achieved through application of zeotropic mixtures, but the component limitations, such as condenser geometry and pressure losses, have serious influences on the optimal composition and concentration of the mixtures.