Radiotherapy is the major means to treat cancer patients. Radiotherapy comprises two phases:rnpretreatment and treatment on radiation machines. This thesis work focuses on the treatment phase.rnTreatment consists of multiple, almost daily irradiation appointments, followed by optionalrnimaging and control assignments. The scheduling of radiotherapy appointments is a complexrnproblem due to various medical and scheduling constraints, such as patient category, machinernavailability, waiting time targets and also due to the size of the problem (i.e., number of machines,rnfacilities and patients). The objective of this thesis is to minimize waiting time and maximizerndevice utilization in) patientâ€™s appointment scheduling. Thus, this thesis presents an optimizationrnalgorithm for scheduling of radiotherapy treatments for categorized cancer patients. In order tornmanage patient information effectively in digital data format a web application is built. This webrnapplication registers users (professionals) that are responsible to register patients and includes arndatabase to store patientâ€™s information. Following this, custom genetic algorithm (GA) isrndeveloped considering constraints primarily patient category and the rest constraints such asrnpatient in date and time, number of fractions, number of machine and also working days andrnworking hour. Moreover, for the GA to be user friendly a desktop application with graphical userrninterface (GUI) is developed. The GUI supports the medical professionals to easily manipulate thernGA parameters such as number of populations, crossover probability, and mutation probabilityrnand also change the dynamic resources or attributes like number of machines, number of patientsrntreated per single machine and number of working days. As a result, the medical professional canrnschedule patients dynamically. In this thesis best GA performances (i.e., fitness value of 88% -rn96.67% accuracy) are obtained for probability crossover (Pc) values between 60% - 80% andrnprobability of mutation (Pm) between 20% - 40%. This means if the health professional sets therncross-over and mutation probability in these ranges, the scheduling will have better optimization,rni.e. prioritize high-risk patients, minimize high risk patient waiting time, thus better care forrnpatients. From the resuls, emergency patients are able to get early treatment than radical patients.rnCompared to traditional manual scheduling, where scheduling is done based on patients arrivalrndate, GA based scheduling enables to prioritize higher risk patients.