Fog computing has emerged as a promising paradigm for meeting the low-latency, high-bandwidth needs of Smart City applications close to data sources. However, existing placement strategies often struggle to jointly optimize timely task completion, efficient resource usage and reduced energy cost. This paper proposes a multi-objective PathFinder Algorithm (PFA) that balances makespan, Fog resource utilization and end-to-end latency in a unified framework. Extensive simulations in iFogSim2 use four baselines, Random, BF-SA, PEAPM and POAPM, evaluated on makespan, power consumption and task guarantee ratio (TGR). The results show that the PFA-based method outperforms the compared approaches across varying Fog network sizes, improving power conservation and overall performance.