<p>Optimal placement of pressure monitoring points in the water distribution network (WDN) is essential for ensuring its efficient and reliable operation. Strategic placement of pressure monitoring points enables the system to have effective surveillance and management. However, the problem of parameter equifinality persists in the optimal placement of pressure monitoring points using intelligent optimization algorithms. Recognizing the inherent uncertainty in nodal water demands, this study utilized the Monte Carlo simulation to analyze network behaviors modelled as a normal distribution. A nodal pressure fluctuation coefficient was introduced to quantify the impact of demand stochasticity on the WDN. This metric was then combined with nodal pressure sensitivity to resolve parameter equifinality in the configuration of pressure monitoring points. An optimization model was developed by integrating these factors, and the Krill Herd Algorithm (KHA) was applied to solve the model and determine the optimal configuration of pressure monitoring points. During the simulation, 20,000 random samples were generated based on the steady-state hydraulic model as benchmarks. By calculating and integrating the nodal pressure fluctuation coefficients and nodal sensitivities, the optimal scheme was determined to resolve the issue of parameter equifinality. Quantitative evaluations demonstrated that the preferred layout outperforms alternative candidate schemes by at least 10% in overall monitoring efficacy. This approach provides a robust framework for the strategic design of pressure monitoring point placement schemes for the WDN.</p>