Currently, electric automobiles are predominantly powered by interior permanent magnet electric machines which possess high efficiency and torque density in comparison to alternative machine types. Although the performance benefits are undisputed, the use of rare earth permanent magnet materials has historically been a commercial risk. To mitigate the potential issues, the automotive industry is considering alternative electric machine designs, which will either use none or a minimal amount of rare earth material. In parallel, synchronous reluctance machines are "magnet-free" electric machine type currently the focus of process industry. Electric power take-off was selected as the application niche for the introduction of synchronous reluctance machine to the automotive sector. The goals of the thesis are to improve the synchronous reluctance machine optimization process by reducing rotor radial cross-section parametric complexity, and to create a software framework for the rotor geometry feasibility validation. The research concentrates on a novel approach of defining rotor geometry on a shape object level, instead of the classical approach which uses geometrical primitives like points, lines, and arcs. This methodology allows simple and robust feasibility validation and robust calculation of at least one point inside every object, which is extremely important for assigning the material to different regions in the finite element analysis tool. The presented solution can be applied on any electric machine type. Furthermore, the thesis defines a set of absolutely feasible synchronous reluctance machine rotor geometries using a minimal set of parameters in order to reduce design complexity. Consequently causing a reduction of overall optimization time due to the smaller number of parameters that define the optimization problem. After selecting the geometry and commencing optimization, the optimization system (software code) must be able to detect if generated geometry is unfeasible (e.g., rotor barriers are overlapping). One of the proposed solutions is a novel concept of forced feasibility, where every unfeasible design is forced to change parameters until reaching feasibility. Otherwise, the infeasible candidate is rejected, which can affect the optimization convergence. Both approaches were compared concluding that forced feasibly causes faster optimization convergence. Scientific contribution of the thesis is reflected in: 1. Robust feasibility and region detection algorithm based on the shape object approach in the definition of electrical machine geometry. 2. Improvement of optimization convergence through the reduction of feasible geometry search-time for synchronous reluctance machines using novel forced feasibility approach 3. Method for synchronous reluctance machine rotor geometry parametrization with the reduced parameter set.