Most manufacturing sector businesses utilize advanced control mechanisms to sustain their ongoing operations. An alarm management system is one of these control mechanisms that works as a safety barrier, and it contains alarm messages indicating abnormal situations to operators. The causes of alarms mainly result in a harmful state of operations that should be eliminated as quickly as possible to minimize possible negative results. However, the size of the system, lack of people directing the system, and process-dependent peak conditions may lead operators to miss some critical alarms. Quality and quantity of products, job safety, and operational costs are some of the features negatively affected by these missing alarms. The proposed work aims to combine a well-established alarm management philosophy with advanced data analytics techniques to optimize decision variables in alarm management processes. This study introduces a novel data-driven optimization method that leverages the Tennessee Eastman Process as a benchmark to validate its effectiveness. The proposed method aims to ensure continuous alarm system health by contributing to the automation of the parameter optimization process in the life cycles of alarm management systems. Key contributions include the development of a method to associate disturbances with alarms, the creation of an alarm simulation platform, and the improvement of alarm parameters through a unique optimization approach. The results show that there is a trade-off between alarm reaction delay, which refers to the time between disturbances and the first relevant alarm and number of alarms and alarm on times. This trade-off can be evaluated in the desired direction by taking into account the priorities of the process.