Composite material is a combination of two or more materials to achieve significant properties. Natural fiber reinforced polymer composite is mostly used composite due to its highest strength-to-weight ratio. The techniques of joining these composite materials have become a crucial issue, i.e., the need for a stronger joint has significantly grown. However, joining geometry parameter optimization of the composite had failed miserably, which leads to excessive or insufficient use of joining geometry parameters. The aim of the study was to optimize the process parameters of single lap adhesive and bolted joint date palm fiber reinforced polyester composite (DPFRPC) for improving the tensile strength. The tensile properties of DPFRPC were determined experimentally with various fiber loading (20, 30, and 40%) and orientations (0/0, 45/-45, and 0/90 degree); the highest tensile strength of DPFRPC, 145MPa, was identified with 30% fiber loading and 0-degree fiber orientation. Adhesive joining was made with overlapping length (24, 40, and 56 mm), width (20, 28, and 36 mm) and adhesive thickness (0.5, 0.75 and 1 mm), whereas bolt joining was done with edge to diameter ratio (2.5, 3.5, and 4.5), width to diameter ratio (1.5, 2.5, and 3.5), and fiber orientation (0, 45, and 90 degree) using L9 orthogonal array to conduct the experiment. An artificial neural network (ANN) model was developed for adhesive and bolt joint to relate the input parameters and output response. The fitness function of the developed ANN model was used in a genetic algorithm (GA) to optimize the process parameters. According to ANN-GA, the optimum parameters of adhesive joint DPFRPC were, 56 mm overlapping length, 36 mm width and 0.95 mm adhesive thickness, with a load carrying capacity of 9.48kN. Similarly, for bolt joining, the optimum parameters were determined using ANN-GA to be 3.5 edge to diameter ratio, 4.5 width to diameter ratio and 56.5 degrees fiber orientation, with load carrying capacity of 9.52kN. Confirmation test was conducted using the optimal parameters of the ANN-GA model, and the result indicates that the predicted and the confirmation test result was within acceptable error range. In general, DPFRPC had an excellent strength-to-weight ratio, and bolt joining was shown to be the best joining mechanism for DPFRPC. Keywords: DPF, DPFRPC, adhesive joining, bolt joining, ANN, GA