Fault-controlled carbonate oil reservoirs boast substantial reserves concentrated along fracture zones and exhibit high initial oil well production capacities. However, these reservoirs are also prone to sharp declines in production. It is imperative to identify the optimal displacement strategy to supplement reservoir energy and elevate recovery efficiencies. The complexity of reservoir characteristics, including fractures, vugs, pores, and other heterogeneous spaces, complicates the assessment of the technical and economic suitability of various displacement techniques. This paper introduces a multi-objective intelligent optimization algorithm derived from particle swarm optimization (PSO), targeting a specialized model of fault-controlled carbonate rock oil reservoirs. By considering different displacement agents and injection parameters, the algorithm formulates multiple objective functions that encompass both technical and economic aspects. Employing the PSO algorithm to ascertain optimal parameters, this study delineates the techno-economic adaptability of disparate displacement techniques. Our findings indicate that in non-severe leaky fault-controlled reservoir models, water flooding emerges as the most cost-effective displacement method during the initial and intermediate stages of development. For typical fault-controlled reservoir models, nitrogen-enriched foam flooding stands out as the most advantageous technique in terms of techno-economic viability. Meanwhile, for ultra-deep, high-pressure reservoirs, carbon dioxide miscible flooding exhibits a degree of practicality.