Recently the literature of Simulation Optimization for solving stochastic constrained optimization problems has been substantially increasing. From the optimization perspective, the population based meta-heuristics algorithms, such as Differential Evolutionary (DE), has shown tremendous success in solving continuous constrained optimization problems. While from the simulation and stochastic perspectives, Monte-Carlo Simulation alongside Chance Constrained Programming (CCP), is one of the most successful combined approaches to handle constraint uncertainty, also called chance constraints. Risk levels are used in CCP to bound the stochastic constraints infeasibility. However, the determination of the risk levels is not precise and is based on subjective personal judgments; due to the lack of a scientific approach to determine their values especially in critical problems that require the minimum possible risk levels. Furthermore, reliability in such problems is a vital requirement, especially for studies under worst-case scenarios. Therefore, we propose a novel approach, called Two-stage simulation assisted Differential Evolutionary for Chance constrained programming algorithm (Two-stage DE-CCP), with two major contributions. The first contribution aims to obtain minimum boundaries for the chance constrains’ risk levels, by utilizing the meta-heuristics DE implicit capabilities with a two stages modification. While the second contribution proposed as a Reliable Chance Constrained Programming (RCCP) with a novel scheme for chance constraints’ feasibility checking, that is called Extreme Scenario Feasibility Checking (ESFC). This name reveals that ESFC aims to obtain more reliable solutions, through comparing the extreme/worst-case scenarios of the chance constraints. The proposed algorithm is applied under normal CCP feasibility checking and ESFC for RCCP, while a comparison between them is performed and an empirical Reliability ratio (Rr) is proposed. The experimental results on benchmark problems and real application show that the proposed algorithm is able to provide decision makers with the minimum possible risk levels. While a substantially high Rr is obtained from the proposed RCCP, as compared to traditional CCP.