Corrosion of oil and gas equipment remained a critical industry problem with annual losses of $1.372 billion. High pressure high temperature (HPHT) conditions significantly complicated the development of effective corrosion inhibitors due to increased environmental aggressiveness and degradation of traditional protective compounds. The aim of this study was to provide a theoretical analysis of the potential application of differential evolution method (DEoptim) for multi-objective optimisation of corrosion inhibitor parameters under extreme HPHT conditions. The research methodology comprised systematic literature review using Scopus, Web of Science, and Google Scholar databases (2019-2024), comparative analysis of computational modelling methods, theoretical analysis of optimisation approaches, and information synthesis for formulating recommendations. The work systematised modern approaches to computational modelling of inhibitors, including quantum chemical calculations, molecular dynamics, and machine learning. An analysis of specific challenges in HPHT environments, where temperatures exceeded 150 °C and pressure exceeded 69 MPa, was conducted. The advantages of evolutionary algorithms for navigating complex multidimensional parameter spaces of inhibitors were considered. The theoretical foundations for using DEoptim for simultaneous optimisation of inhibition efficiency, thermal stability, environmental acceptability, and economic feasibility were substantiated. The analysis demonstrated that DEoptim offered superior robustness and multi-objective capabilities compared to traditional gradient-based and genetic algorithms, particularly for HPHT applications. The study proposed a novel integration concept combining DEoptim with quantum chemical calculations and machine learning to create hybrid optimisation frameworks, potentially reducing computational costs by up to 60% while improving inhibitor discovery efficiency. The results showed that DEoptim application could provide systematic search for optimal inhibitor formulations, reduce the number of required experiments, and identify non-obvious synergistic component combinations for HPHT applications