An efficient urban development requires a reduction of carbon footprint through innovative and sustainable infrastructure planning. Traditional optimization techniques often suffer from slow convergence and limited energy savings as a result of their extensive parameter tuning without statistical filtering. To address this challenge, an energy-efficient street lighting framework is proposed here by integrating the concept of Analysis of Variance (ANOVA) and Genetic Algorithm (GA) named as ANOVA-GA. Initially, multiple lighting parameters such as pole height, boom angle, boom length, light type, and voltage are evaluated in the standard lighting software Dialux to obtain a dataset that identifies their impact on energy use and the overlap of illumination. These design parameters are statistically analyzed using Type II ANOVA to determine their significance in reducing energy consumption. Only the most impactful parameters evaluated earlier are retained in the GA for optimization. The GA then searches for an optimal combination of these parameters that minimizes energy consumption while meeting lighting standards. By embedding ANOVA, the method reduces the dimensionality of the solution space, and which in turn accelerates convergence. The proposed framework achieves an annual energy consumption of 182 kWh/year, outperforming traditional single and hybrid framework. It reduces the number of poles required per kilometer by 25%. Furthermore, the proposed hybrid optimization framework is spatially validated and utilized by the concept of geographical information system (GIS), where a reduced number of pole counts and wider spacing are achieved without compromising illumination quality. The integration of simulation, optimization, and geospatial planning makes the proposed work highly applicable to obtain significant energy savings for sustainable urban lighting in smart city planning.