Achieving optimal performance of a database can be crucial for many businesses, and tuning its configuration parameters is a necessary step in this process. Many existing tuning methods involve complex machine learning algorithms and require large amounts of historical data from the system being tuned. However, training machine learning models can be problematic if a considerable amount of computational resources and data storage is required. This paper investigates the possibility of using less complex search algorithms or evolutionary algorithms to tune database configuration parameters, and presents a framework that employs Hill Climbing and Particle Swarm Optimization. The performance of the algorithms are tested on a PostgreSQL database using read-only workloads. Particle Swarm Optimization displayed the largest improvement in query response time, improving it by 26.09% compared to using the configuration parameters' default values. Given the improvement shown by Particle Swarm Optimization, evolutionary algorithms may be promising in the field of database tuning.