Organic Rankine Cycles (ORC) use low-temperature heat to generate electrical power. To use the full potential of a heat source, the ORC has to be tailored to the specific application. Tailoring a cycle means an integrated design of both process and working fluid. This integrated design leads to complex mixed-integer nonlinear program (MINLP) optimization problems. To avoid this complexity, working fluid candidates are commonly preselected using heuristic guidelines; subsequently, the process is optimized for the set of preselected working fluids. However, the preselection can fail, leading to suboptimal solutions.An approach for integrated design of ORC process and working fluid is the Continuous-Molecular Targeting–Computer-aided Molecular Design (CoMT-CAMD) approach. CoMT-CAMD employs the physically-based Perturbed-chain Statistical Associating Fluid Theory (PC-SAFT) equation of state as thermodynamic model of the working fluid. In PC-SAFT, each working fluid is described by a set of pure component parameters. In a first step, the so-called CoMT step, the discrete pure component parameters are relaxed resulting in a hypothetical optimal working fluid and the corresponding optimal process. In a second step, real working fluids with similar properties are identified using Computer-aided Molecular Design and a second-order Taylor approximation of the objective function around the hypothetical optimum. So far, the process models in CoMT-CAMD were implemented in a procedural programming language, which hinders the reusability, the use for more complex processes and dynamic simulations.In this work, we integrate CoMT-CAMD into the object-oriented modelling language Modelica. For this purpose, Modelica is directly linked to PC-SAFT. Thereby, already existing model libraries for Modelica can be used to model the ORC process. The resulting design approach is applied to the integrated design of an ORC process and working fluid for a geothermal power station.