The capital market is the meeting place of supply and demand. The profit orientation possible through the stock market stimulates two processes: 1) buying or 2) selling financial instruments – a long or short option. Investing is a process accompanied by fluctuations – often of <1% per day. Hence, individual investors look for alternatives, which include derivatives that fluctuate up to 100% per day. Therefore, the need was perceived to develop an instrument – a valuation tool – to help individual investors make investment decisions. The Black-Scholes Model (BSM) uses six independent variables. It was therefore decided to compile an alternative valuation model based on the Genetic Algorithm (GA) on the strength of companies listed on NASDAQ: FaceBook, Apple, Amazon, Netflix and Google (so-called FAANG companies), using Eureqa GA software. The purpose of this paper is to present the results of a study that attempts to develop a more efficient option pricing model by comparing the accuracy of the Genetic Algorithm (GA) and the Black-Scholes Model (BSM) and evaluating gaps in underlying price movements. The comparison of the genetic algorithm with the traditional Black-Scholes option pricing model led to the development of a new linear investment model – investors can make predictions using one variable – the share price, which should significantly optimise strategic investment decisions. The presented model is characterised by higher investment efficiency, especially important for individual investors, who usually are not able to achieve the profit scale effect based on the value of a retail investment portfolio.