Kriging Neighbourhood Analysis (KNA) is a common method in geostatistics for determining the configuration and search parameters that directly affect the prediction quality of kriging methods during resource estimation. However, traditional KNA is often a labour-intensive and time-consuming trial-and-error process that optimises one parameter at a time, potentially missing global optima due to the complexity of parameter interdependencies. This paper proposes an enhanced methodology, Genetic Kriging Neighbourhood Analysis (GKNA), which leverages genetic algorithms to evaluate multiple parameters simultaneously in KNA. The integration of evolutionary computation provides a more robust framework for navigating the multi-dimensional search space, identifying interaction effects among parameters, and arriving at a solution more efficiently. This approach significantly reduces the manual iteration required in traditional KNA, speeds up the analysis process, more effectively explores the optimisation search space, and potentially improves prediction accuracy. We discuss the benefits, issues, application, and potential of the GKNA methodology, demonstrating through a case study how it can support practitioners in effectively handling the complex task of spatial configuration and search parameter optimisation.